Predicting and Attending to Damaging Collisions \\for Placing Everyday Objects in Photo-Realistic Simulations
\name{Aly Magassouba \textsuperscript{a
Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. This problem is particularly challenging because it is necessary to predict what happens if an object is placed in a cluttered designated area. We show that a rule-based approach that uses plane detection, to detect free areas, performs poorly. To address this, we develop PonNet, which has multimodal attention branches and a self-attention mechanism to predict damaging collisions, based on RGBD images. Our method can visualize the risk of damaging collisions, which is convenient because it enables the user to understand the risk. For this purpose, we build and publish an original dataset that contains 12,000 photo-realistic images of specific placing areas, with daily life objects, in home environments. The experimental results show that our approach improves accuracy compared with the baseline methods.
interactnlmsample.tex
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\title{Predicting and Attending to Damaging Collisions \\for Placing Everyday Objects 
in Photo-Realistic Simulations}

\author{
\name{Aly Magassouba \textsuperscript{a}\thanks{CONTACT Aly Magassouba.
Email: aly.magassouba@nict.go.jp} and Komei Sugiura\textsuperscript{ab} and Angelica Nakayama\textsuperscript{a} and Tsubasa Hirakawa\textsuperscript{c} and Takayoshi Yamashita\textsuperscript{c} and Hironobu Fujiyoshi\textsuperscript{c} and Hisashi Kawai\textsuperscript{a} }
\affil{\textsuperscript{a}National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika, Soraku, Kyoto 619-0289, Japan;\\ \textsuperscript{b}Keio University, 3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japan;\\ \textsuperscript{c}Chubu University, 1200 Matsumotocho, Kasugai, Aichi 487-8501, Japan} }

\maketitle

\begin{abstract}
 Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. This problem is particularly challenging because it is necessary to predict what happens if an object is placed in a cluttered designated area. We show that a rule-based approach that uses plane detection, to detect free areas, performs poorly. To address this, we develop PonNet, which has multimodal attention branches and a self-attention mechanism to predict damaging collisions, based on RGBD images. Our method can visualize the risk of damaging collisions, which is convenient because it enables the user to understand the risk. For this purpose, we build and publish an original dataset that contains 12,000 photo-realistic images of specific placing areas, with daily life objects, in home environments. The experimental results show that our approach improves accuracy compared with the baseline methods.
\end{abstract}

\begin{keywords}
Attention branch network; Photo-realistic simulation; Domestic service robots; Physical inference
\end{keywords}

\input{section1_introduction}
\input{section2_related}
\input{section3_pb}
\input{section4_method}
\input{section5_dataset}
\input{section6_experiments}
\input{section7}


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% \bibitem{Jen05}%1
% Jenkins~PF. Making sense of the chest x-ray: a hands-on guide. New York (NY):
%   Oxford University Press; 2005.

% \bibitem{Sch02}%2
% Schott~J, Priest~J. Leading antenatal classes: a practical guide. 2nd ed.
%   Boston (MA): Books for Midwives; 2002.

% \bibitem{Wen95}%3
% Wenger~NK, Sivarajan~Froelicher~E, Smith~LK, et~al. Cardiac rehabilitation.
%   Rockville (MD): Agency for Health Care Policy and Research (US); 1995.

% \bibitem{Sha78}%4
% Shakelford~RT. Surgery of the alimentary tract. Philadelphia (PA): W.B.
%   Saunders; 1978. Chapter 2, Esophagoscopy; p. 29--40.

% \bibitem{AG98}%5
% Ambudkar~SV, Gottesman~MM, editors. {ABC} transporters: biomedical, cellular,
%   and molecular aspects. San Diego (CA): Academic Press; 1998. (Methods in
%   enzymology; vol. 292).

% \bibitem{Smi75}%6
% Smith~CE. The significance of mosquito longevity and blood-feeding behaviour in
%   the dynamics of arbovirus infections. Med Biol. 1975;53:288--294.

% \bibitem{Men05}%7
% Meneton~P, Jeunemaitre~X, de~Wardener~HE, et~al. Links between dietary salt
%   intake, renal salt handling, blood pressure, and cardiovascular diseases.
%   Physiol Rev. 2005 Apr;85:679--715.

% \bibitem{DCK03}%8
% Dostorovsky~JO, Carr~DB, Koltzenburg~M, editors. Proceedings of the 10th World
%   Congress on Pain; 2002 Aug~17--22; San Diego, CA. Seattle: IASP Press; c2003.

% \bibitem{Hor98}%9
% Horrobin~DF, Lampinskas~P. The commercial development of food plants used as
%   medicines. In: Prendergast~HD, Etkin~NL, Harris~DR, et~al., editors. Plants
%   for food and medicine. Proceedings of the Joint Conference of the Society for
%   Economic Botany and the International Society for Ethnopharmacology; 1996
%   Jul~1--6; London. Kew (UK): Royal Botanic Gardens; 1998. p. 75--81.

% \bibitem{Ant03}%10
% Antani~S, Long~LR, Thoma~GR, et~al. Anatomical shape representation in spine
%   x-ray images. Paper presented at: VIIP 2003. Proceedings of the 3rd IASTED
%   International Conference on Visualization, Imaging and Image Processing;
%   2003 Sep~8--10; Benalmadena, Spain.

% \bibitem{Zha05}%11
% Zhao~C. Development of nanoelectrospray and application to protein research and
%   drug discovery [dissertation]. Buffalo (NY): State University of New York at
%   Buffalo; 2005.

% \bibitem{Rog05}%12
% Roguskie~JM. The role of \emph{Pseudomonas aeruginosa} 1244 pilin glycan in
%   virulence [master's thesis]. [Pittsburgh (PA)]: Duquesne University; 2005.

% \bibitem{SRW05}%13
% Savage~E, Ramsay~M, White~J, et~al. Mumps outbreaks across England and Wales
%   in 2004: Observational study. BMJ. 2005;330(7500):1119--1120 [cited 2005
%   May~31]; Available from: http://bmj.bmjjournals.com/cgi/reprint/330/7500/1119.

% \bibitem{Gau05}%14
% Gaul~G. When geography influences treament options. Washington Post (Maryland
%   Ed.). 2005 Jul~24;Sect.~A:12 (col.~1).

% \bibitem{BGC04}%15
% Berrino~F, Gatta~G, Crosignani~P. [Case-control evaluation of screening
%   efficacy]. Epidemiol Prev. 2004 Nov--Dec;28:354--359. Italian.

% \bibitem{PI51}%16
% Piaget~J, Inhelder~B. La gen{\`e}se de l'id{\'e}e de hasard chez l'enfant
%   [The origin of the idea of chance in the child]. Paris: Presses
%   Universitaires de France; 1951.

% \end{thebibliography}
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% \bibitem{Jen05}%1
% Jenkins~PF. Making sense of the chest x-ray: a hands-on guide. New York
%  (NY): Oxford University Press; 2005.

% \bibitem{Sch02}%2
% Schott~J, Priest~J. Leading antenatal classes: a practical guide. 2nd
%  ed. Boston (MA): Books for Midwives; 2002.

% \bibitem{Wen95}%3
% Wenger~NK, Sivarajan~Froelicher~E, Smith~LK, et~al. Cardiac
%  rehabilitation. Rockville (MD): Agency for Health Care Policy and
%  Research (US); 1995.

% \bibitem{Sha78}%4
% Shakelford~RT. Surgery of the alimentary tract. Philadelphia (PA):
%  W.B. Saunders; 1978. Chapter 2, Esophagoscopy; p. 29--40.

% \bibitem{AG98}%5
% Ambudkar~SV, Gottesman~MM, editors. {ABC} transporters: biomedical,
%  cellular, and molecular aspects. San Diego (CA): Academic Press; 1998.
%  (Methods in enzymology; vol. 292).

% \bibitem{Smi75}%6
% Smith~CE. The significance of mosquito longevity and blood-feeding
%  behaviour in the dynamics of arbovirus infections. Med Biol.
%  1975;53:288--294.

% \bibitem{Men05}%7
% Meneton~P, Jeunemaitre~X, de~Wardener~HE, et~al. Links between dietary
%  salt intake, renal salt handling, blood pressure, and cardiovascular
%  diseases. Physiol Rev. 2005 Apr;85:679--715.

% \bibitem{DCK03}%8
% Dostorovsky~JO, Carr~DB, Koltzenburg~M, editors. Proceedings of the
%  10th World Congress on Pain; 2002 Aug~17--22; San Diego, CA. Seattle:
%  IASP Press; c2003.

% \bibitem{Hor98}%9
% Horrobin~DF, Lampinskas~P. The commercial development of food plants
%  used as medicines. In: Prendergast~HD, Etkin~NL, Harris~DR, et~al.,
%  editors. Plants for food and medicine. Proceedings of the Joint
%  Conference of the Society for Economic Botany and the International
%  Society for Ethnopharmacology; 1996 Jul~1--6; London. Kew (UK): Royal
%  Botanic Gardens; 1998. p. 75--81.

% \bibitem{Ant03}%10
% Antani~S, Long~LR, Thoma~GR, et~al. Anatomical shape representation in
%  spine x-ray images. Paper presented at: VIIP 2003. Proceedings of the
%  3rd IASTED International Conference on Visualization, Imaging and
%  Image Processing; 2003 Sep~8--10; Benalmadena, Spain.

% \bibitem{Zha05}%11
% Zhao~C. Development of nanoelectrospray and application to protein
%  research and drug discovery [dissertation]. Buffalo (NY): State
%  University of New York at Buffalo; 2005.

% \bibitem{Rog05}%12
% Roguskie~JM. The role of \emph{Pseudomonas aeruginosa} 1244 pilin
%  glycan in virulence [master's thesis]. [Pittsburgh (PA)]: Duquesne
%  University; 2005.

% \bibitem{SRW05}%13
% Savage~E, Ramsay~M, White~J, et~al. Mumps outbreaks across England and
%  Wales in 2004: Observational study. BMJ. 2005;330(7500):1119--1120
%  [cited 2005 May 31]; Available from:
%  http://bmj.bmjjournals.com/cgi/reprint/330/7500/1119.

% \bibitem{Gau05}%14
% Gaul~G. When geography influences treament options. Washington Post
%  (Maryland Ed.). 2005 Jul~24;Sect.~A:12 (col.~1).

% \bibitem{BGC04}%15
% Berrino~F, Gatta~G, Crosignani~P. [Case-control evaluation of screening
%  efficacy]. Epidemiol Prev. 2004 Nov--Dec;28:354--359. Italian.

% \bibitem{PI51}%16
% Piaget~J, Inhelder~B. La gen{\`e}se de l'id{\'e}e de hasard chez
%  l'enfant [The origin of the idea of chance in the child]. Paris:
%  Presses Universitaires de France; 1951.

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% Please note that the class file calls up the open-source \LaTeX\ packages booktabs.sty, epsfig.sty and rotating.sty, which will, for convenience, unpack with the downloaded template and class file. The template calls for natbib.sty and subfig.sty, which are also supplied for convenience.


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% This article template, the \texttt{interact} class file and the associated open-source \LaTeX\ packages are also available via e-mail. Requests should be addressed to \texttt{latex.helpdesk@tandf.co.uk}, clearly stating for which journal you require the template and class file.

\end{document}

section1_introduction.tex
\section{Introduction
\label{intro}
}
Domestic service robots (DSRs) are promising solutions to the shortage of labor for supporting older adults and disabled people.
Supporting functions that are required from DSRs involve manipulation tasks \cite{smarr2014domestic,billard2019trends}. Placing everyday objects in designated areas is crucial for DSRs \cite{brose2010role} because it is involved in many daily life tasks such as placing a glass on a table and putting a fruit on counter top. Thus, predicting potential collisions in such motions is highly beneficial for many DSR applications; particularly collisions that may put the user or the robot at risk, or damage the environment. 

In this study, we consider the problem of predicting damaging collisions before motion execution. This problem is addressed as a classification task: we infer the likelihood of damaging collisions given a visual scene in which to place an object. A typical use case is shown in Fig. \ref{fig:architecture}.
This problem is particularly challenging because it is necessary to predict what happens if an object is placed in a cluttered designated area. Furthermore, in physical space, not all collisions are damaging. A slight push on an obstacle, or placing an object on a flat obstacle is not damaging. Thus, damaging collisions highly depend on the physics of the objects that are interacting in the environment, which is complex to predict. % in advance. 


\begin{figure}[tp]
   \centering
      \includegraphics[width=0.8\columnwidth]{fig/eye_catch_iros20.png}
      \caption{Use case of PonNet: predicting damaging collisions before the placing motion of a DSR.}
   \label{fig:architecture}
 \end{figure}
 
There have been many attempts to evaluate the risk of collisions using data-driven \cite{gualtieri2018pick, magassouba2018multimodal} or rule-based \cite{harada2014validating} approaches.  Although traditional rule-based approaches can partially estimate possible collisions, the estimation generally requires the 3D models of obstacles that are not always available beforehand. On the other hand, they are not required by our method. It can predict collision risk only from camera images. 

To achieve this, we propose PonNet which uses an attention branch network (ABN) \cite{ABN_Fukui}. The ABN is an image classifier, inspired by class activation mapping \cite{zhou2016cvpr} structures, that generates attention maps. The ABN is composed of an attention branch that highlights the most salient portions in an image, given a label to predict.  

In this paper, we extend the ABN by introducing multiple attention branches and an additional self-attention mechanism to predict damaging collisions. Unlike the initial ABN structure, PonNet is a multimodal network that uses RGB and depth inputs: multiple attention branches focus on different parts of these inputs. An additional self-attention mechanism is used to fuse the features extracted from each attention branch. We apply PonNet to the damaging collision prediction (DCP) problem, and compare it with baseline methods. A demonstration video is available at this URL\footnote{\protect\url{https://youtu.be/L-36HZEqFuU}}

The following are our key contributions:
\begin{itemize}
\item We propose PonNet which has multiple attention branches and an additional self-attention mechanism to fuse the output of the attention branches. We explain the method in Section \ref{method}.
\item We build an original dataset BILA-12K with 12,000 designated areas in which to place everyday objects. We explain this dataset in Section \ref{dataset}.
\item We apply PonNet to the DCP problem where a simulated DSR places everyday objects on furniture pieces. We explain the experimental validation in Section \ref{exp}.
\end{itemize}


% Local Variables:
% eval: (auto-fill-mode -1)
% coding: utf-8
% End:

section2_related.tex

\section{Related Work
\label{related}
}

Data-driven approaches for manipulation have been widely investigated in robotics \cite{billard2019trends,watanabe2017survey,magassouba2019understanding}. The approach proposed in \cite{jiang2012learning}  trains a classifier to predict the placement suitability of candidate poses based on 3D point-clouds of the object and the environment. The classifier evaluates physical feasibility and stability in addition to human placement preference. The authors of \cite{zeng2018robotic} introduced a 
robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. In this work, the robot is endowed with collision avoidance using several motion primitives. Similarly, the authors of \cite{gualtieri2018pick} proposed a system  that uses deep reinforcement learning to address novel objects. The proposed collision avoidance system extracts areas that are free of obstacles.
Most of these works either assume collision-free areas \cite{jiang2012learning} or build specific planning tasks to avoid obstacles \cite{zeng2018robotic, gualtieri2018pick}. However, the latter case provides ad hoc solutions because these planning tasks are adapted to a particular environment configuration, which is why fixed environment setups are used.

A collision prediction method would complement these previous works and improve their generalizability in unknown environments. For this purpose  a collision test algorithm was proposed in \cite{harada2014validating}. This method clusters the working space based on convexity/concavity, contact, and equilibrium properties between the object and environment. However, such an approach requires a deep knowledge of the environment. Thus, the algorithm must be reinitialized for each novel environment.
In our previous work \cite{magassouba2018multimodal}, we addressed the placing task through a classifier, MMC-GAN, to predict the suitability of a designated area from an initial linguistic instruction. Although a collision risk likelihood was implicitly learned by the network, this method was not sufficiently refined to determine the damaging collision risks. 


PonNet is a network that predicts and allows the visualization of potential damaging collisions from a visual scene. Damaging collisions depend on the physical properties of the objects interacting in the environment and it was shown in \cite{mottaghi2016happens} that neural networks could learn these properties from visual inputs. PonNet architecture is based on attention branch mechanisms \cite{ABN_Fukui} that allow the visualization of the predicted damaging collisions.  

In contrast to attention mechanisms that are generally integrated into the main network in an unsupervised manner,  attention branches are dedicated branches that generate an attention map from the output labels. An attention branch is designed to focus on specific regions of a feature by generating an attention map. This attention map is then used to improve the performance of the network.
Such an approach was used in our previous works \cite{magassouba2019multi} and \cite{magassouba2020multimodal} in which linguistic and visual modalities were utilized to generate fetching instructions and predict a target to fetch.
Unlike these works, PonNet is based on visual inputs only: RGB and depth modalities are used to predict damaging collisions. Hence, PonNet architecture  can be referred to as an RGBD fusion method. These methods are generally designed for object recognition and detection \cite{chen2018progressively, yang2019adaptive}.

Another contribution of this study is the BILA-12K dataset for DCP. BILA-12K is specifically designed for DSRs because it was collected in simulated home environments (see Section \ref{dataset}). DCP tasks depend on perception and physical reasoning for which PHYRE  \cite{bakhtin2019phyre} and CLEVR \cite{johnson2017clevr} datasets have been proposed respectively. However, these datasets are not suitable for the present study. PHYRE considers only two-dimensional environments whereas CLEVR uses only geometric objects in a single environment: neither furniture pieces nor everyday objects are considered.   
section3_pb.tex
\section{Problem Statement
\label{problem}
}
\begin{figure}[t]
\centering
\subfloat{\includegraphics[width=0.30\columnwidth]{fig/falling1.png}} \hspace{0.01 em}
\subfloat{\includegraphics[width=0.30\columnwidth]{fig/falling2.png}} \hspace{0.01 em}
\subfloat{\includegraphics[width=0.30\columnwidth]{fig/falling3.png}}
\caption{What happens if the robot places the rubik's cube on the shelf? A chain of collision events resulted in a damaging collision. }
\label{fig:placing}
\centering
\end{figure} 



\subsection{Task Background}
The typical scene of a placing task, in Fig. \ref{fig:placing}, illustrates the challenges of predicting damaging collisions.
In this example, a fundamental problem is to predict the following: ``What happens if the robot places the Rubik's cube on the shelf?''  
The robot pushed  several objects: a black pot collided with an hourglass and  made it fall from the shelf resulting in a damaging collision. Predicting this chain of events is particularly challenging, because it is necessary to understand the physics of objects and predict the different interactions between them.

Furthermore, in a placing task, several collisions may happen but not all of them can be considered as damaging. In the example illustrated above, slight touches were allowed, however, the collision between the black pot and the hourglass was damaging. In this study, a damaging collision is defined as a collision between two objects, where the absolute relative speed $|\mathbf{v}|$ is greater than a threshold $v_{\text{DC}}$. 


These characteristics make it difficult to predict damaging collisions based on classic approaches that use a robot model or free areas. A simple method based on plane detection achieves low prediction accuracy, which is explained in detail in Section \ref{exp}. Furthermore, we aim to visualize the risk of collision as probability, which is convenient because this enables the user to understand the risk and stop dangerous motions in advance. 

\subsection{Task Description: Damaging-Collision Prediction}
Our target task is to predict the likelihood of damaging collisions when DSRs place everyday objects in designated areas. Because the prediction is performed in advance, no video sequence of the placing motion is available. In this paper, we define a DCP problem as follows:
\begin{description}
    \item[Input:] RGBD image of the destination
    \item[Output:] Likelihood of damaging collisions. 
\end{description}
We define the terms used in this study as follows:
\begin{description}
    \item[Target:] The object (e.g., fruits and snacks) that is placed on a destination.
    \item[Destination:] The furniture piece (e.g., shelf) on which the target is placed.
    \item[Obstacle:] An object (e.g., bottle) that is already on the destination. 
\end{description}
We assume that the robot is in front of the destination with the target in its hand. Therefore, the robot can see the destination and reach it. As a result, the placing motion consists of simply extending the robot arm to put down the target. It should be noted that it may be technically possible to introduce force control with a more sophisticated control and planning task. However, solving planning tasks is not our focus. Instead, our study aims at DCP for simple placing strategy. If the likelihood of a damaging collision is above a threshold, then the robot can place the target using a simple strategy. Otherwise, more sophisticated strategies can be chosen, although such strategies may be highly specific to the task environment. For similar reasons, deformable objects are not considered in this study.

We assume that the targets and obstacles are daily life objects. The target candidate should be handled by DSRs, and should be graspable. The  obstacles, targets and destinations are explained more thoroughly in Section \ref{dataset}.
Because the goal of this study is DCP, the evaluation metric is the prediction accuracy for unseen samples.




\subsection{Task Environment}
To make the experimental results reproducible, the simulation environment shown in Fig. \ref{fig:placing} is used. Using a simulation makes the data collection and labeling process faster, whereas with real robots, we must set every scene manually and have a human perform the labeling process. Additionally, a simulated environment is safer for the robot and user: in particular, the evaluation of damaging collisions may be dangerous as the robot collides with its environment.

We use a standardized robot, HSR \cite{yamamoto2018human} with RGBD cameras, as the DSR platform. HSR is emulated through a customized simulator. The simulator extends SIGVerse \cite{inamura2013development} that was used in the World Robot Summit 2018 Partner Robot Challenge/Virtual Space competition. In addition to emulating HSR components following ROS architecture, our simulator is augmented with photo-realistic environments (see Section \ref{dataset}) in configurations similar to those used for DSRs competitions such as RoboCup@Home\cite{I15}. 

\begin{figure*}
\includegraphics[width=1.0\textwidth]{fig/ponNet.png}
\caption{Proposed method architecture: PonNet is composed of two attention branches (RGB and depth)  and an additional self-attention mechanism in the perception branch. `ResNet-18 (pre)' represents layers 1-12 of ResNet-18 whereas `ResNet-18~(post)' refers to layers 12-18. Global average pooling (GAP) layers are used for feature dimension reduction in addition to fully connected (FC) layers. }
\label{fig:structure}
\centering
\end{figure*}
section4_method.tex

\section{Proposed Method
\label{method}
}









\subsection{Novelty}
The PonNet architecture extends the ABN by introducing RGB and depth branches and an additional self-attention mechanism to predict the likelihood of a damaging collision. In this configuration, the RGB and depth modalities complement each other. Indeed, for instance, the depth modality is insensitive to intra-object edges or flat objects unlike the RGB modality but may be noisy. By contrast, transparent objects (e.g., empty bottles) may be detected better using the RGB modality through color variation information.

 %In the DCP (damaging-collision prediction) task, the labels correspond to damaging collision (DC) and no damaging collision (NDC).
Thus, PonNet is composed of two attention branches (see Fig. \ref{fig:structure}): RGB and depth attention branches. 
The attention branches  highlight the most salient part of the visual features that are related to the prediction of damaging collision. These features are then input to a perception branch. 
Thereafter, the perception branch fuses the RGB and depth features to predict the likelihood of a damaging collision.
 PonNet has the following characteristics:  
\begin{itemize}
\item PonNet extends the classic RGBD two-stream CNN \cite{chen2018progressively, gupta2014learning} with attention branches.
\item PonNet is composed of multiple attention branches to handle complementary information. The branches are fused by an additional self-attention layer. 
\item Each attention map overlaid on the image input provides a visual explanation of the collision label predicted from each branch.
\end{itemize}
%These characteristics are more thoroughly detailed in the following section.





\subsection{Architecture}
\subsubsection{Network Input}
As mentioned above, PonNet takes as input RGB and depth images of the destination, where the robot is expected to place a given target.
Given a destination image $i$, the set of inputs ${\bf x}(i)$ of PonNet is defined as follows:
\begin{equation}\label{eq:input}
    {\bf x}(i)= \{{\bf x}_r(i), {\bf x}_d(i), {\bf x}_h(i) \},
\end{equation}
where ${\bf x}_r(i)$  and ${\bf x}_d(i)$ denote the RGB and depth images, respectively, of the destination area, and ${\bf x}_h(i)$ represents heuristic knowledge about the placing task configuration.  In the following, the index $i$ is omitted for simplicity.  Because we assume that the placing motion is fixed, a placing area is given in advance as a region of interest (ROI) in the destination image. Thus, inputs ${\bf x}_r$  and ${\bf x}_d$ are images of the placing area. Inspired by computer vision literature, the depth input ${\bf x}_d$ is initially transformed into a colorized image based on surface normals \cite{aakerberg2017depth} (see Fig.~\ref{fig:structure}). This method allows us to process a depth image with a similar network architecture as conventional a RGB image. Input ${\bf x}_h$ contains the width, height and length of the target in addition to the camera height. This information is obtained when the robot grasps the target. 


\subsubsection{Attention Branches}
PonNet is composed of two attention branches (see Fig. \ref{fig:attention}) that generate attention maps ${\bf a}_r$ and ${\bf a}_d$ for the RGB and depth modalities, respectively. From these attention maps, the most salient areas of ${\bf x}_r$ and ${\bf x}_d$ are emphasized to predict the likelihood of a damaging collision.

The attention branches are initially input with features maps ${\bf f}_r$  and ${\bf f}_d$  obtained using a backbone CNN feature extractor. We use ResNet-18 as the base network: the feature maps are obtained from the 12$^{th}$ layer. Thereafter, similar to the ABN architecture \cite{ABN_Fukui}, these features maps are processed through residual block layers. In each branch, the output of the residual blocks is processed with a convolutional layer with two kernels, followed by a batch normalization (BN) layer. The first kernel is trained to emphasize features related to damaging collisions whereas the second kernel emphasizes non-damaging collision features. The convolutional layer is then averaged using a global average pooling (GAP) layer from which the likelihood of damaging collisions is predicted. Cross-entropy-based losses $J_{r}$ and $J_{d}$ are minimized by the two attention branches. 

In parallel, an attention map is obtained from an attention block layer. The attention block is composed of a convolutional layer with a single kernel followed by ReLU and sigmoid functions. Weighted feature maps ${\bf w}_r$ and ${\bf w}_d$ are obtained using: 
\begin{equation}\label{equ:w_map}
\begin{aligned}
    {\bf w}_{r}&= (\mathbf{1}+ {\bf a}_{r}) \odot {\bf f}_{r}\\
    {\bf w}_{d}&= (\mathbf{1}+ {\bf a}_{d}) \odot {\bf f}_{d},
    \end{aligned}
\end{equation}
where $\odot$ refers to the Hadamard product. %The features ${\bf w}_{r}$ and ${\bf w}_{d}$ are then processed by a second backbone CNN network, a GAP for dimension reduction, to respectively output ${\bf o}_{r}$ and ${\bf o}_{d}$.
\begin{figure}[t]
\includegraphics[width=1.0\columnwidth]{fig/ab.png}
\caption{Attention branch architecture: An attention map ${\bf a}_k$ is obtained from convolutional (Conv.) layers that are enhanced using batch normalization (BN).  The output  ${\bf w}_k$, with $k \in \{r,d\}$, is obtained from \eqref{equ:w_map} which is represented as `$ \circledcirc $'. `Conv. n' represents convolutional layers of dimensions 14 $\times$ 14 $\times$ n.}


\label{fig:attention}
\centering
\end{figure}

\subsubsection{Perception Branch}
In the perception branch, ${\bf w}_{r}$ and ${\bf w}_{d}$ are processed by the backbone CNN ResNet-18 and a GAP for dimension reduction. The features ${\bf o}_{r}$ and ${\bf o}_{d}$ are then obtained from the subsequent fully connected (FC) layer. These features are combined to predict the likelihood of a damaging collision. A classic method consists of concatenating these two features. However, simply concatenating them may limit the accuracy of PonNet, because, for each sample, the depth and RGB images contain different information. Instead, we use a self-attention layer \cite{hori2017attention} which selectively uses the depth and RGB features to improve DCP accuracy. The output ${\bf m}$ of the self-attention layer is given by
\begin{equation}
    {\bf m} = \alpha_r {\bf o}_{r}  + \alpha_d  {\bf o}_{d}, \label{att weight}
\end{equation}
where $\alpha_r$ and $\alpha_d$ are parameters trained by the network. These parameters are computed as follows:
    \begin{equation}\label{balancing}
        \alpha_k  = \frac{\exp{(e_{k})}}{\sum_{j=1}^K \exp{(e_{j})}}, \\
    \end{equation}
and  
 \begin{equation}\label{weights}
        e_{k} = {\bf V}^{T} \tanh{({\bf W}_{k} {\bf o}_{k} + {\bf b}_k)},
\end{equation}
where the index $k \in \{r, d\}$ denotes the given input modality. The weight matrix ${\bf W}_{k}$, ${\bf V}$ and bias vector ${\bf b}_k$ are learnable parameters.

Finally, ${\bf x}_h$, which characterizes the camera height and the size of the target, is input to the network. The likelihood $p({\bf y})$ of a damaging collision  is then obtained by processing  the concatenation of ${\bf m}$ and ${\bf x}_h$ in FC layers. In the same manner as the attention branches, the cross-entropy loss $J_{p}$ is minimized by the perception branch.

\subsubsection{Network Loss}
To predict the likelihood $p({\bf y})$ of a damaging collision, the loss function $J_{total}$ of the network is given by 
\begin{equation}\label{equ:J_p}
J_{total} = \lambda_r J_{r} + \lambda_d J_{d} + \lambda_p J_{p},
\end{equation}
where $\lambda_r$, $\lambda_d$ and $\lambda_p$ denote the different loss weights.
Given $J$ as a generic notation for $J_{r}$, $J_{d}$ and $ J_{p}$, the cross-entropy loss is expressed as follows
\begin{align} \label{equ:J}
    J &= -\sum_n \sum_{m} y^{*}_{nm} \log p(y_{nm}),
\end{align}
where $y^{*}_{nm}$ denotes the label given to the $m$-th dimension of the $n$-th sample, and $y_{nm}$ denotes its prediction.    
The attention and perception branches predict the same labels. 



% Local Variables:
% eval: (auto-fill-mode -1)
% coding: utf-8
% End:

section5_dataset.tex

\section{Dataset: BILA-12K
\label{dataset}
}
\begin{figure}[!h]
\subfloat{\includegraphics[width=0.489\columnwidth]{fig/environment1.png}} \enskip
\subfloat{\includegraphics[width=0.489\columnwidth]{fig/environment2.png}} \\\vskip -0.1pt
\subfloat{\includegraphics[width=1\columnwidth]{fig/furniture.png}}\\\vskip -0.1pt
\subfloat{\includegraphics[width=1\columnwidth]{fig/obstacles.png}}
\caption{Simulator environment setup. Top-row figures show the placing motion in different conditions. Middle and bottom-row figures illustrate samples of the destinations and obstacles/targets.}
\label{fig:env_setup}

\centering
\end{figure}

\subsection{Dataset Construction Overview}
The top row figures in Fig. \ref{fig:env_setup} show the simulation environment used in data collection. 
The dataset BILA-12K (available at this URL\footnote{\protect\url{https://komeisugiura.jp/software/bila12k/latest.tgz}}) was collected from a simulated home environment, as described in Section \ref{problem}. Simulated environments allow the automatic collection of large-scale data in various configurations. BILA-12K can be described as follows: % This is crucial to generalize PonNet and ensure that it does not overfit to a predefined configuration.  
\begin{itemize}
\item BILA-12K was collected from photo-realistic environments based on Unity engine. Each environment was procedurally generated with everyday objects and furniture as illustrated in Fig. \ref{fig:env_setup}.
\item BILA-12K is composed of 12,000 RGBD samples of designated areas in which a target can be placed.
\item BILA-12K contains the collision statistics for all samples through the placing motion performed by a standardized robot, HSR. A video of the placing motion was also recorded for further analysis and verification.
\end{itemize}
The procedure given below describes the steps used to build this dataset.


%The statistics of BILA-12K are reported in Table \ref{tab:data_coll}.  
% Each of these steps will be detailed in the following sections.
% \begin{itemize}
% \item[$1$.] Setup an area with everyday objects as described above.
% \item[$2$.] Record the scene with the robot embedded cameras (RGB and depth).
% \item[$3$.] Place the target on the destination.
% \item[$4$.] Record the collision statistics.
% \end{itemize} 

% \begin{table}[b]
% \normalsize
% \caption{\small BILA-12K collection statistics }
% \label{tab:data_coll}
% \centering
% \begin{tabular}{l|c|c}
% \hline
%  & $\#$Instance &Examples\\
% \hline
% Locations & 12& Kitchen, lobby \\
% \hline  
% Destinations &6 & Table, shelf\\
% \hline
% Obstacles &28& Camera, bottle \\
% \hline
% Targets& 15& Fruit, soda \\
% \hline
% \end{tabular}
% \end{table}
\subsection{Procedure}
\begin{enumerate}[label=\arabic*)]
	\item{\bf Environment Setup:}\label{proc:one}
As shown in Fig. \ref{fig:env_setup}, the simulator procedurally generated home environment. For more variety, we considered 12 distinct locations, with various background and lighting conditions, distributed among different rooms. BILA-12K is composed of 12,000 samples, that is, 1,000 samples for each location.
	\item{\bf Destination:}
For each sample, HSR's position was initialized in front of a randomly selected destination. A destination corresponds to everyday furniture such as tables, shelves and sofas. Each destination has a different shape, material and reflection properties. A set of six destination candidates was considered. It should be noted, that destinations such as shelves have multiple levels that could be selected as placing areas.
	\item{\bf Obstacles:} %Obstacles were scattered on each destination.
 The obstacles refer to daily life objects in the environment. From a pool of 28 objects, a random number of obstacles were randomly scattered on the destination. To increase the variation of obstacle positions and orientations, all obstacles were dropped from a given height.
\item{\bf Target:}
Similar to obstacles, a target was selected from a set of 15 daily objects that are graspable by HSR.
\item{\bf Placing Motion:}
HSR recorded through its RGBD camera the state of the destination as a 640 $\times$ 480 image, in addition to the camera height and target dimension. Thereafter, the robot could perform the placing motion (please see the accompanying video) by extending its arm. The collision statistics were then recorded for each task. As our focus is on DCP, no placing strategy nor obstacle avoidance were implemented. Similarly, the simulator did not handle slip or friction. These parameters are out of the scope of this study.
\item{\bf Repeat:}
The procedure was repeated from step 1) until a sufficient number of samples was collected.
\end{enumerate}


\subsection{Labeling Strategy}
Each placing motion was then automatically labeled by the simulator. Two labels, DC (damaging collision) and NDC (no damaging collision), were used for the DCP task.
The damaging collision speed threshold was defined as $v_{\text{DC}}$~=~$0.1$~$\text{m/s}$. 
If collisions above this threshold were detected, the sample was labeled DC. Otherwise the sample was labeled NDC. To evaluate PonNet,
BILA-12K was randomly split into training (10,000 samples), validation (1,000 samples) and test (1,000 samples) sets. After removing invalid samples, we  obtained the statistics summarized in Table \ref{tab:dataset}.
% \begin{itemize}
% \item collisions between the robot arm and some obstacles,
% \item collisions between the robot and the target destination,
% \item collisions between several obstacles,
% \item collisions between the target and some obstacles.
% \end{itemize}
% Hence, from the characteristics of damaging collisions, predicting the outcome of a placing task becomes challenging. Indeed, for instance,  a plain motion that slightly touches some obstacles with a speed below 0.1 m/s is labelled as a successful $A1$. Using the robot 3D model or detecting free areas does not allow to predict accurately such cases, as damaging collisions highly depend on the physics of the objects interacting in the environment. 

\begin{table}[t]
\caption{ Statistics for the PonNet dataset where two labels DC (damaging collision) and NDC (no damaging collision) are used to characterize collisions}
\label{tab:dataset}
\centering
\begin{tabular}{c|ccc|c}
\hline
$\#$ & Train& Valid.& Test& $\sum$([\%])\\
\hline
DC&4807&492&448& 5747 (48.7) \\

NDC&5074&496&490& 6060 (51.3)\\
\hline
$\sum$([\%])& 9881 (83.7) & 988 (8.4)& 938 (7.9)&11807 (100)\\

\hline
\end{tabular}


\end{table}


% \begin{figure}[ht]
% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg}
% \caption{Success}
% \label{fig:success}
% \end{subfigure}

% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/scoring/0003_rgb.jpg}
% \caption{Failure}
% \label{fig:fail}
% \end{subfigure}

% \caption{Scoring and labeling samples(mock images)}
% \label{fig:scoring}
% \end{figure}





% Local Variables:
% eval: (auto-fill-mode -1)
% coding: utf-8
% End:

section6_experiments.tex
\section{Experiments
\label{exp}
}



\subsection{Experimental Setup}

%From 640$\times$480 image inputs, a placing area was cropped as a 192$\times$192 image patch and rescaled to 224$\times$224. 
The input images were rescaled to 224$\times$224.
Each of them contains the region where the robot hand is placing the target, and was extracted from the camera image.

%\Update
%This placing area was cropped in the center of the image where the interaction occurred for training and evaluation purpose. However, in real situation PonNet can be applied to any area.\Done
The CNN backbone of PonNet was based on the ResNet-18 network  as mentioned in the method section. It was initialized with ImageNet \cite{russakovsky2015imagenet} weights and fine-tuned on BILA-12K. 
The feature maps that were input to the two attention branches were extracted from the 12$^\text{th}$ layer of ResNet-18. Each feature map, ${\bf f}_r$ and  ${\bf f}_d$, had the dimensions 14$\times$14$\times$256. 
The attention branches were composed of three residual blocks with full pre-activation \cite{he2016identity}. To comply with the feature map dimensions, the convolutional layers in the attention branches had the dimensions 14$\times$14. The second CNN corresponded to the remaining lower layers of ResNet-18. More specifically, the processed features of dimensions 14$\times$14$\times$256 were input to the network (12$^\text{th}$ layer), and the features of dimensions 7$\times$7$\times$512 were output from the 18$^\text{th}$ layer. From a GAP and a flattening process, a feature vector of dimension 512 was obtained.
The attention layer used weights of dimensions 256$\times$256. The self-attention block is followed by FC layers of size 256, 16 and 2. 

The loss weights were empirically set to 1 for the attention branches and $0.3$ for the perception branch. PonNet had approximately 18 M parameters and was trained on a machine equipped with a Tesla V100 with 32 GB of GPU memory, 768 GB RAM and an Intel Xeon 2.10 GHz processor. The results were reported after 150 epochs when the training loss was reduced by approximately 98\%. With this setup, it took around one and a half hours to train PonNet with a batch size of 48 samples and a learning rate of 5$\times$10$^{-4}$. The parameters of PonNet are summarized in Table \ref{tab:param}. 

\begin{table}[b]
\caption{ Parameter settings and structures of PonNet}\label{tab:param}
\centering
\begin{tabular}{|c|l|}
\hline
PonNet & Adam (Learning rate= $5e^{-4}$, \\
 Opt. method  &$\beta_1=0.99$, $\beta_2=0.9$) \\
\hline
Network input size & $[224\times224]$\\
%ROI size & $[192\times192]$\\
\hline
Backbone CNN  & ResNet-18\\
\hline
Res. Block  & full pre-activation\\
\hline
 & Input: $[14\times14\times256]$  \\
 \cline{2-2}
 Attention  & Conv.layer : $[14\times14\times2]$  \\
\cline{2-2}
 Branch & Att. layer: $[14\times14\times1]$  \\
 \cline{2-2}
 & Output:   $[14\times14\times256]$ \\
\hline
Perception& Att. layer : 256 \\
 \cline{2-2}
Branch& FC: 256, 16, 2 \\
\hline
Weight   &$\lambda_d=1, \lambda_r=1$,  $\lambda_p=0.3$  \\
\hline
 Batch size & 48  \\
 \hline
\end{tabular}
\end{table}


\subsection{Quantitative Results}
We compared PonNet with a baseline method: classic RGBD plane detection algorithm \cite{wang2018plane} (available at this URL\footnote{\protect\url{https://github.com/chaowang15/RGBDPlaneDetection}}) for free area detection. We also performed several ablation studies with  different PonNet types using no attention, i.e., ResNet-50 only (type1) and ResNet-18 only (type2) and a single attention branch \cite{ABN_Fukui} (type3). Because these methods are inherently monomodal approaches, RGB only, depth only and RGBD inputs were tested. It should be noted that for the RGBD configuration, RGB and depth inputs were concatenated channel-wise, through an early fusion scheme. %Similar to PonNet, ResNet-only-based methods were initialized using ImageNet weights and fully trained on our dataset. 
Additionally a variant of PonNet without self-attention (type4) in the perception branch was also tested. Except for the plane detection methods, all of the above methods have exactly the same inputs, i.e., RGB and colorized depth images.


All the above methods were evaluated for test set accuracy when the best validation accuracy was obtained. Hence, these results reflect more objectively the accuracy of each method on unseen data. The quantitative results of all methods are summarized in Table \ref{tab:results} which reports the average accuracy and  standard deviation for five trials. 
In the table, the ``BB'' column shows the backbone network as either of ResNet-50 (``50'') or ResNet-18 (``18''). 
The ``AB'' column shows the type of attention branch as either of single ABN (``S'') or multiple ABN (``M'').
The ``SA'' column shows whether self attention is introduced or not.


\begin{table}[t]
\caption{Average test set accuracy and standard deviation over five trials for DCP.  
The ``BB'' column shows the backbone network as either of ResNet-50 (``50'') or ResNet-18 (``18''). 
The ``AB'' column shows the type of attention branch as either of single ABN (``S'') or multiple ABN (``M'').
The ``SA'' column shows whether self-attention is introduced or not.}

\label{tab:results}
\centering
\begin{tabular}{l|c|c|c|c|c}
\hline
\multicolumn{1}{l|}{\bf Method} & BB & AB & SA &\multicolumn{1}{c|}{\bf Input} & \multicolumn{1}{c}{\bf Accuracy}  \\
\hline
\hline
 Plane detect. & $-$ & $-$ & $-$ & RGBD & 82.5\\
 \hspace{2mm}(baseline) & & & & & \\
\hline
\multicolumn{1}{c|}{} &&&&RGB & 88.25$\pm$0.45\\
PonNet-type1  & 50 & $-$ & $-$ & D &87.89$\pm$0.88\\
\multicolumn{1}{c|}{} &&&& RGBD & 88.58$\pm$0.67\\
\hline
 \multicolumn{1}{c|}{} &&&& RGB &88.82$\pm$0.68\\
PonNet-type2 & 18 & $-$ & $-$ & D &88.42$\pm$0.22\\ \multicolumn{1}{c|}{}&&&&RGBD& 89.25$\pm$0.33\\
\hline
\multicolumn{1}{c|}{} &&&& RGB &89.85$\pm$0.45\\
PonNet-type3  & 18 & S & $-$ & D &89.51$\pm$0.82\\
\multicolumn{1}{c|}{}  &&&& RGBD&90.18$\pm$0.11\\
\hline
PonNet-type4 & 18 & M & N & RGBD & 90.47$\pm$0.32\\
\hline
PonNet (ours) & 18 & M & Y & RGBD &{\bf90.94$\pm$0.22}\\
\hline
\end{tabular} 

\end{table}
    

The best accuracy was obtained by the proposed architecture, PonNet, with an average of 90.94$\%$ of correct predictions. Our method outperformed the baseline plane detection method by $8.4\%$. Because the plane detection method did not have variation in output, the standard deviation is not shown. Furthermore, the ablation study emphasized the contribution of each part of the network. We observed that the attention branch mechanism improved the prediction accuracy compared to the PonNet-type1 and PonNet-type2. Furthermore, PonNet-type2 performed better than PonNet-type1: we hypothesize that the number of layers of ResNet-18 was more adequate for our dataset. Using several branches instead of a single one is also relevant as the accuracy increased. Similarly, the self-attention mechanism improved PonNet accuracy from 90.47\% to 90.94\%. 

\begin{figure}[!b]
\begin{imgrows}%
\imgrow\label{fig:one}
  \includegraphics[width=0.3\columnwidth]{fig/TP.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/good_rgb2.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/good_depth2.jpg} \vskip 1pt
\imgrow\label{fig:two}
  \includegraphics[width=0.3\columnwidth]{fig/TN.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/good_rgb4.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/good_depth4.jpg} \vskip 1pt
\imgrow\label{fig:three}
  \includegraphics[width=0.3\columnwidth]{fig/FP.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/FP_RGB.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/FP_depth.jpg} \vskip 1pt
\imgrow\label{fig:four}
  \includegraphics[width=0.3\columnwidth]{fig/FN.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/FN_RGB.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/FN_depth.jpg}
\end{imgrows}

\caption{Qualitative results of DCP using PonNet.  The first column is the input image whereas the second and third columns are the RGB and depth attention maps. (a) True positive, (b) True negative, (c) False positive, (d) False negative.}

\label{fig:qualitative}
\end{figure}

Additionally, as expected, our ablation study demonstrated that multimodal approaches outperformed monomodal approaches regardless of the network tested. However, RGBD fusion through PonNet architecture was more efficient. This result emphasizes that RGB and depth modalities complement each other, as claimed initially. 


\subsection{Qualitative Results}

% \begin{figure}[t]
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/good_rgb1.jpg}}\enskip
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/good_rgb2.jpg}}\enskip
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/good_rgb3.jpg}}\enskip\\
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/good_rgb4.jpg}}\enskip
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/bad_rgb.jpg}}\enskip
%   \subfloat{\includegraphics[width=0.325\columnwidth]{fig/bad_rgb2.jpg}}\\\vskip -10pt
%   \setcounter{subfigure}{0}
%   \subfloat[$y$=DC, $\hat{y}$=DC]{\label{fig:one}\includegraphics[width=0.325\columnwidth]{fig/good_depth1.jpg}}\enskip 
%   \subfloat[$y$=DC, $\hat{y}$=DC]{\label{fig:two}\includegraphics[width=0.325\columnwidth]{fig/good_depth2.jpg}}\enskip 
%   \subfloat[$y$=NDC, $\hat{y}$=NDC]{\label{fig:three}\includegraphics[width=0.325\columnwidth]{fig/good_depth3.jpg}}\enskip 
%   \subfloat[$y$=NDC, $\hat{y}$=NDC]{\label{fig:four}\includegraphics[width=0.325\columnwidth]{fig/good_depth4.jpg}}\enskip 
%   \subfloat[$y$=DC, $\hat{y}$=NDC]{\label{fig:five}\includegraphics[width=0.325\columnwidth]{fig/bad_depth.jpg}}\enskip 
%   \subfloat[$y$=NDC, $\hat{y}$=DC]{\label{fig:six}\includegraphics[width=0.325\columnwidth]{fig/bad_depth2.jpg}}
%     \caption{\small Qualitative results of DCP prediction for PonNet.  The first row illustrates the RGB attention maps while the second is based on depth.}
%   \label{fig:qualitative}
% \end{figure}






PonNet architecture embeds attention branches that allow visual explanation. Such a feature is particularly desirable for analyzing the functioning of neural networks that are typically considered as black boxes. Fig.~\ref{fig:qualitative} illustrates the attention of PonNet output by the RGB and depth attention branches for a given input. Figs.~\ref{fig:qualitative}(a) and (b) show accurate predictions, whereas Figs.~\ref{fig:qualitative}(c) and (d) illustrate the failure cases.  The attention maps of the RGB and depth modalities are given for each case.
These results emphasize a key point of PonNet: the visualization of the predicted damaging collisions, where the most salient areas of a given image are in red. The attention maps for the RGB and depth images were particularly different and can be interpreted in the following manner:
\begin{itemize}
    \item RGB modality attends to the obstacles that may lead to damaging collisions. As illustrated in Fig.~\ref{fig:qualitative}(a), small and flat obstacles (e.g., cigarette) are not attended unlike the teddy bear that lead to a damaging collision.
    \item Depth modality attends to the areas in which the target can be placed safely. In Fig.~\ref{fig:qualitative}(b) there is no risk of collision in the area attended: as observed during the placing task, a slight touch made the soda can roll gently. By contrast, no area is attended in Fig. \ref{fig:qualitative}(a) because the target cannot be placed safely.
\end{itemize}
Interestingly, in addition to giving understandable feedback to users, these attention maps may be used to predict the most suitable areas for the placing task given several candidate areas in the same manner as that in \cite{magassouba2018multimodal}. In the same way, a future study may be about the evaluation  of the attended areas  with a network structure where the target shape is input to the attention branches.


Regarding erroneous predictions, in Fig.~\ref{fig:qualitative}(c) attention provided contradictory outputs: from the depth there was a low likelihood of damaging collision whereas it was the opposite for RGB that focused of the cup. The PonNet self-attention mechanism favored the RGB modality in this case and erroneously predicted a damaging collision. In Fig.~\ref{fig:qualitative}(d) both modalities failed to predict the damaging collision with the cigarette box (in blue): unlike the case illustrated in Fig.~\ref{fig:qualitative}(a), this obstacle was standing on its edge and lead to a damaging collision when knocked down.
This emphasizes the challenge of DCP tasks: the same object in a slightly different position may lead to two different outcomes.





\subsection{Error Analysis}
PonNet erroneous predictions were also quantitatively analyzed. The confusion matrices reported in Table \ref{tab:conf},  show the classification results  for PonNet in addition to the detailed predictions of the RGB and depth attention branches. 




By analyzing the PonNet results, we observed that most erroneous cases were false-positive predictions: a damaging collision was predicted whereas this is not actually the case. These errors made the system more conservative about the collision risk for the placing motion. By contrast, there were only 32 false-negative predictions. These errors are more crucial in a placing task. Despite this, they represented only 3.5\% of all predicted samples, which is reasonable considering the complexity of the DCP task. Regarding the RGB and depth results, they show that the attention branches alone performed differently and with a lower accuracy. Despite this, false-negative predictions were greatly reduced (-20\%) by fusing both modalities as performed in PonNet.


\begin{table}[t]
\normalsize
\caption{Confusion matrices of PonNet, RGB and depth attention  branches. ${\bf y}$ and $\widehat{\bf y}$ represent the ground truth and prediction, respectively.}\label{tab:conf}
\centering
\begin{tabular}{c|cc|cc|cc}
\hline
  \multirow{2}{*}{\diagbox{${\bf y}$}{$\widehat{\bf y}$}}&\multicolumn{2}{c|}{\bf Total}&\multicolumn{2}{c|}{\bf RGB Att.}&\multicolumn{2}{c}{\bf Depth Att.}\\
\cline{2-7}
&DC &NDC &DC &NDC &DC &NDC \\
\hline
 DC & 458& 32 & 450& 40 & 450& 40\\
 NDC  & 60& 388& 72& 376& 64& 384\\
\hline
\end{tabular}
\end{table} 
\subsection{Generalization ability}
PonNet generalization ability was also assessed qualitatively from the samples. This analysis purpose is to determine whether PonNet simply detects objects (i.e., a given obstacle always predicted as DC or NDC) or PonNet is really able to predict the physical interactions between different objects, as initially intended. To this end, samples containing the same obstacles were evaluated. Some of these samples are illustrated in Fig. \ref{fig:generalization}  to support our claims. Each row refers to a given obstacle. For the sake of simplicity, we represent only  RGB attention maps. For this modality areas of higher risk of damaging collisions are attended. All these samples were correct (true-positive and true-negative).

In the first row, the tea pot is an obstacle that is highly likely to lead to DC. However, the middle sample is predicted as NDC: in this case there is enough space to place the target without colliding with the obstacle.
The second row, about the white cup, illustrates three different behaviors. In the left sample, DC is unlikely as the obstacle is upward, in equilibrium and there is enough space. In the two other samples, the obstacle is lying down. Although the middle sample is correctly predicted as NDC, the attention is slightly higher: the object was able to roll without any damaging collision. In the right sample, damaging collisions occurred,  because the cup rolled and hit the toy car. This  could be  predicted and captured by PonNet.
The third rows, shows a green can obstacle in different positions. When laying down, only the left sample is predicted DC as this obstacle is a central position where the target is supposed to be placed, which may lead to a strong collision. In the right sample case, this obstacle is slightly shifted and is able to roll when touched during the placing motion. When the can is upwards, it generates a DC as it is correctly predicted in the middle sample.

These results suggest that PonNet is able to generalize the DCP task and does not overfit to obstacles. Indeed, it can be inferred that physical properties of the obstacles are learned by the network. 

\begin{figure}[t]
\begin{imgrows}%
\imgrow\label{fig:tea}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0041.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0208.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0255.jpg} \vskip 1pt
\imgrow\label{fig:cup}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0106.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0107.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0241.jpg} \vskip 1pt
\imgrow\label{fig:can}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0060.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0718.jpg}
  \includegraphics[width=0.3\columnwidth]{fig/ATT-RGB-0907.jpg} 
\end{imgrows}

\caption{Attended damaging collision regions (red) for different scene configurations with similar obstacles. Each row refers to a given obstacle  (a) Black tea pot, (b) White cup , (c) Green can.}
\label{fig:generalization}
\end{figure}



\subsection{Collision Type Analysis}
In a more extensive study, the different types of damaging collisions were also analyzed. Four additional types of damaging collisions were labeled from the simulator. The collision types can be described as follows:
\begin{itemize}
\item ({\bf AO}) Collision between the robot arm and an obstacle.
\item ({\bf TO}) Collision between the target and an obstacle.
\item ({\bf OO}) Collision between multiple obstacles.
\item ({\bf OD}) Collision between an obstacle and the destination.% (e.g knockdown obstacle),
\item ({\bf Any}) All types of damaging collisions.
\end{itemize}
It should be noted that for each sample one or several types of damaging collisions may occur. The results are given in Table~\ref{tab:collisions} for the average test set accuracy and standard deviation over five runs. In this set of experiments, we introduced the sum of each type of collision loss into the global loss function. Therefore, the accuracy of ({\bf Any}) collision was different from the results in Table \ref{tab:results}.  Please note that the plane detection method cannot solve such a task, and was therefore not tested for this set of experiments. 

 In comparison with the initial DCP task, predicting each type of collision is more complex. The accuracy of each of these collisions drastically decreased compared with the results obtained in Table \ref{tab:results}. In particular, it was relatively complex to predict damaging collisions related to ({\bf TO}). This is challenging because ({\bf TO}) collisions depend on the speed and the shape of the target in addition to the force of the robot motion. % Similarly, ({\bf AO}) collisions require the network to predict which obstacles may be knocked down (as opposed to obstacles that may roll) as in in Fig. \ref{fig:qualitative} (d).
Although, ({\bf Any}) collisions are correlated with the other collisions, the difference in accuracy may be explained by the strong non-linearity between modalities, intra-branches (see Table \ref{tab:conf}) and between each collision branches, inter-branches. Furthermore, We assume that the different PonNet architectures and loss functions were not optimized to predict several types of collision at the same time. Indeed, We observed that the prediction accuracy of ({\bf Any}) collision, for all PonNet types, was decreased compared with our first set of experiments. Future studies about the architectures and the intra- and inter-branches relations may help to improve this kind of task.

\begin{table*}
\setlength\tabcolsep{2.8pt}
\caption{Average test set accuracy and standard deviation over five trials for different types of collisions considering the different ablations of PonNet. }
\label{tab:collisions}
\small
\begin{tabular}{l|c|c|c|c|c|c}
\hline
 & {\bf Input} &\multicolumn{5}{c}{\bf Collision type }  \\
\cline{3-7}
 \multicolumn{1}{l|}{\bf Method }  &{\bf  Type}& {\bf Any}& {\bf AO} &{\bf TO}&{\bf OO} & {\bf OD}\\
\hline 
 &RGB &87.74 $\pm$ 0.53 &79.00 $\pm$ 1.51 & 78.02 $\pm$ 0.65& 81.30 $\pm$ 0.73 & 80.42 $\pm$ 0.60\\
 PonNet-type1  &D & 86.38 $\pm$ 0.65 & 77.83 $\pm$ 0.70 &77.16 $\pm$ 0.72& 82.46 $\pm$ 1.01 & 80.21 $\pm$ 0.33\\
 &RGBD&88.05 $\pm$ 0.44 &79.22 $\pm$ 0.65 &77.96 $\pm$ 0.36& 81.14 $\pm$ 0.59 & 81.11 $\pm$ 0.94\\
 \hline
 &RGB &87.35 $\pm$ 1.29 &80.21 $\pm$ 0.52 &77.39 $\pm$ 0.59& 82.58 $\pm$ 1.56 & 81.96 $\pm$ 1.09\\
PonNet-type2 &D & 88.29 $\pm$ 0.60 & 80.93 $\pm$ 0.30 &77.19 $\pm$ 0.87& 82.67 $\pm$ 0.99 & 81.82 $\pm$ 0.53\\
 &RGBD&89.15 $\pm$ 0.46 &80.79 $\pm$ 1.68 &76.84 $\pm$ 1.49& 82.13 $\pm$ 0.21 & 82.10 $\pm$ 0.39\\
 \hline
 &RGB &88.94 $\pm$ 0.66 &79.50 $\pm$ 1.07 &76.44 $\pm$ 1.23& 81.43 $\pm$ 0.63 & 81.77 $\pm$ 0.79\\
 PonNet-type3  &D&87.93 $\pm$ 0.39 &77.48 $\pm$ 1.33 &74.01 $\pm$ 1.14& 82.02 $\pm$ 0.36 & 80.85 $\pm$ 1.81\\
 &RGBD&89.89 $\pm$ 0.76 &79.48 $\pm$ 0.95 &75.04 $\pm$ 1.60& 81.98 $\pm$ 0.74 & 82.13 $\pm$ 1.46\\
\hline
 PonNet-type4 &RGBD&89.90 $\pm$ 0.43 &79.18 $\pm$ 0.27 &76.96 $\pm$ 1.36& 81.96 $\pm$ 0.78 & 82.01 $\pm$ 0.89\\
 \hline
 PonNet&RGBD& 90.10 $\pm$ 0.33 &80.10 $\pm$ 0.12 & 77.25 $\pm$ 0.94& 83.01 $\pm$ 0.59& 82.11 $\pm$ 0.11\\
\hline
\end{tabular}
\end{table*}

%\subsection{Discussion (to be added after FP analyzed) }
 
% \begin{figure}[ht]

% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_24.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/d-w_feat24.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_w_feat24.jpg}
% \caption{}
% \label{fig:comp24}
% \end{subfigure}

% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_25.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/d-w_feat25.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_w_feat25.jpg}
% \caption{}
% \label{fig:comp25}
% \end{subfigure}

% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_74.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/d-w_feat74.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_w_feat74.jpg}
% \caption{}
% \label{fig:comp74}
% \end{subfigure}

% \begin{subfigure}{0.45\textwidth}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_130.jpg} 
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/d-w_feat130.jpg}
% \includegraphics[width=0.3\linewidth, height=2cm]{fig/qualitative_results/rgb_w_feat130.jpg}
% \caption{}
% \label{fig:comp130}
% \end{subfigure}

% \caption{Qualitative results (Original image, Depth attention feature map and RGB attention feature map (mock images)}
% \label{fig:qualitative}
% \end{figure}

% Local Variables:
% eval: (auto-fill-mode -1)
% coding: utf-8
% End:

section7.tex

\section{Conclusion}

Motivated by the increasing demand for DSRs, we developed a method to predict damaging collisions when DSRs are placing daily life objects in designated areas. In this study, we made the following contributions:
\begin{itemize}
\item We proposed PonNet which can predict damaging collisions from camera images obtained before placing motions. Unlike existing methods, PonNet has multiple attention branches and an additional self-attention mechanism to fuse the output of these branches.
\item We validated PonNet using a large-scale dataset for the DCP task. PonNet outperformed the baseline plane detection method by $8.4\%$. 
\end{itemize}
Future studies include combining this work with linguistic explanation.


% Local Variables:
% eval: (auto-fill-mode -1)
% coding: utf-8
% End:

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% Encoding: UTF-8

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  pages={1463--1477},
  year={2014},
}


@inproceedings{wang2018plane,
  title={{Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes}},
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  year={2018}
}

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}



@inproceedings{inamura2013development,
  title={{Development of Robocup@Home Simulation Towards Long-term Large Scale HRI}},
  author={Inamura, T. and Tan, J T C and Sugiura, K. and Nagai, T. and Okada, H.},
  booktitle={Robot Soccer World Cup},
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  year={2013},
  organization={Springer}
}

@article{smarr2014domestic,
  title={{Domestic Robots for Older Adults: Attitudes, Preferences, and Potential}},
  author={Smarr, C-A. and Mitzner, T.L  and others},
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  number={2},
  pages={229--247},
  year={2014},
}


@article{brose2010role,
  title={{The Role of Assistive Robotics in the Lives of Persons with Disability}},
  author={Brose, S. W. and Weber, D. J. and others},
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}

@article{billard2019trends,
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  year={2019},
}


@incollection{yamamoto2018human,
  title={{Human Support Robot (HSR)}},
  author={Yamamoto, T. and Nishino, T. and Kajima, H. and Ohta, M. and Ikeda, K.},
  booktitle={ACM SIGGRAPH},
  pages={1--2},
  year={2018}
}

@article{magassouba2019understanding,
  title={{Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target-Source Classification}},
  author={Magassouba, A. and Sugiura, K. and  Trinh Quoc, A. and Kawai, H.},
  journal={IEEE RA-L},
 year={2019},
 volume={4},
 number={4},
 pages={3884-3891}
}


@inproceedings{hori2017attention,
  title= {{Attention-based Multimodal Fusion for Video Description}},
  author={Hori, C. and Hori, T. and others},
  booktitle={ICCV},
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  year={2017}
}

@inproceedings{zhou2016cvpr,
    author    = {Zhou, B. and Khosla, A. and Lapedriza, A. and Oliva, A. and Torralba, A.},
    title     = {{Learning Deep Features for Discriminative Localization}},
    booktitle = {CVPR},
    year      = {2016}
}


@inproceedings{chen2018progressively,
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  year={2018}
}

@inproceedings{aakerberg2017depth,
  title={{Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition}},
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  booktitle={IJCCI},
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  year={2017}
}

@inproceedings{gupta2014learning,
  title={{Learning Rich Features from RGB-D Images for Object Detection and Segmentation}},
  author={Gupta, S. and Girshick, R. and Arbel{\'a}ez, P. and Malik, J.},
  booktitle={ECCV},
  pages={345--360},
  year={2014},
  organization={Springer}
}
@Misc{A9,
  Title                    = {{Unmanned Aerial Vehicle Based Flash Flood Monitoring Using Lagrangian Trackers}},

  Author                   = {Abdulaal, Mohammed and Algarni, Mohammed and Shamim, Atif and Claudel, Christian},
  HowPublished             = {International Workshop on Robotic Sensor Networks},
  Year                     = {2014},

  Booktitle                = {International Workshop on Robotic Sensor Networks},
  Timestamp                = {2016.07.10}
}

@InProceedings{A3,
  Title                    = {Evolutionary Learning in the 2D Artificial Life System ``Avida''},
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  Year                     = {1994},

  Address                  = {Cambridge},
  Editor                   = {Brooks, R. and Maes, P.},
  Publisher                = {MIT Press},

  Owner                    = {sugiura},
  Timestamp                = {2007.01.29}
}

@InProceedings{Akahane05IES,
  Title                    = {Autonomous Design of Robot Morphology for Learning Behavior Using Evolutionary Computation},
  Author                   = {Makoto Akahane and Komei Sugiura and Takayuki Shiose and Hiroshi Kawakami and Osamu Katai},
  Booktitle                = {Proceedings of the 2005 Japan-Australia Workshop on Intelligent and Evolutionary Systems (IES2005)},
  Year                     = {2005},

  Address                  = {Hakodate, Japan},
  Month                    = {November},
  Pages                    = {CD-ROM},

  Owner                    = {sugiura},
  Timestamp                = {2006.07.18}
}

@InProceedings{A8,
  Title                    = {Benchmarking through competitions},
  Author                   = {Amigoni, F and Bonarini, A and Fontana, G and Matteucci, M and Schiaffonati, V},
  Booktitle                = {Workshop on Robot Competitions: Benchmarking, Technology Transfer, and Education},
  Year                     = {2013},

  Timestamp                = {2015.12.24}
}

@InProceedings{A7,
  Title                    = {{RT-Middleware: Distributed Component Middleware for RT (Robot Technology)}},
  Author                   = {Ando, Noriaki and Suehiro, Takashi and Kitagaki, Kosei and Kotoku, Tetsuo and Yoon, Woo-Keun},
  Booktitle                = {Proc. IROS},
  Year                     = {2005},
  Pages                    = {3933--3938},

  Timestamp                = {2015.03.05}
}

@InProceedings{A5,
  Title                    = {{Intrigue: Personalized Recommendation Of Tourist Attractions For Desktop And Handset Devices}},
  Author                   = {Liliana Ardissono and Anna Goy and Giovanna Petrone and Marino Segnan and Pietro Torasso},
  Booktitle                = {Applied Artificial Intelligence},
  Year                     = {2003},
  Number                   = {8-9},
  Pages                    = {687--714},
  Volume                   = {17},

  Owner                    = {sugiura},
  Timestamp                = {2013.11.16}
}

@Article{A10,
  Title                    = {{A Survey of Robot Learning from Demonstration}},
  Author                   = {Argall, Brenna D and Chernova, Sonia and Veloso, Manuela and Browning, Brett},
  Journal                  = {{Robotics and Autonomous Systems}},
  Year                     = {2009},
  Number                   = {5},
  Pages                    = {469--483},
  Volume                   = {57},

  Timestamp                = {2016.10.24}
}

@InProceedings{A6,
  Title                    = {{DAvinCi: A Cloud Computing Framework for Service Robots}},
  Author                   = {Arumugam, Rajesh and Enti, Vikas Reddy and Bingbing, Liu and Xiaojun, Wu and Baskaran, Krishnamoorthy and Kong, Foong Foo and Kumar, Appadorai Senthil and Meng, Kang Dee and Kit, Goh Wai},
  Booktitle                = {Proc. ICRA},
  Year                     = {2010},
  Pages                    = {3084--3089},

  Timestamp                = {2014.07.08}
}

@Article{A1,
  Title                    = {Cognitive Developmental Robotics As a New Paradigm for the Design of Humanoid Robots},
  Author                   = {Minoru Asada and Karl F. MacDorman and Hiroshi Ishiguro and Yasuo Kuniyoshi},
  Journal                  = {Robotics and Autonomous Systems},
  Year                     = {2001},
  Pages                    = {185-193},
  Volume                   = {37},

  Owner                    = {sugiura},
  Timestamp                = {2006.07.15}
}

@Article{A2,
  Title                    = {Coevolution of Bodies and Behavior in a Modular Reconfigurable Robot},
  Author                   = {Yuji Asai and Takaya Arita},
  Journal                  = {IPSJ Transactions on Mathematical Modeling and Its Applications},
  Year                     = {2002},
  Number                   = {SIG 10},
  Pages                    = {110-118},
  Volume                   = {43},

  Owner                    = {sugiura},
  Review                   = {ブロック型ロボットで行動と形態の共進化},
  Timestamp                = {2006.09.13}
}

@InProceedings{A4,
  Title                    = {{Real-Time 3D Visual Sensor for Robust Object Recognition}},
  Author                   = {Attamimi, Muhammad and Mizutani, Akira and Nakamura, Tomoaki and Nagai, Takayuki and Funakoshi, Kotaro and Nakano, Mikio},
  Booktitle                = {Proceedings of IROS},
  Year                     = {2010},
  Pages                    = {4560--4565},

  Owner                    = {sugiura},
  Timestamp                = {2013.02.25}
}

@InProceedings{Balakrishnan96,
  Title                    = {On Sensor Evolution in Robotics},
  Author                   = {Karthik Balakrishnan and Vasant Honavar},
  Booktitle                = {Proceedings of the First International Conference on Genetic Programming},
  Year                     = {1996},

  Address                  = {Stanford University, CA.},
  Pages                    = {455-460}
}

@Article{B8,
  Title                    = {The dynamics of adaptive behavior: A research program},
  Author                   = {Beer, RD},
  Journal                  = {Robotics and Autonomous Systems},
  Year                     = {1997},
  Number                   = {2-4},
  Pages                    = {257--289},
  Volume                   = {20},

  Publisher                = {Elsevier},
  Timestamp                = {2009.12.07}
}

@InProceedings{B13,
  Title                    = {A robot path planning framework that learns from experience},
  Author                   = {Berenson, Dmitry and Abbeel, Pieter and Goldberg, Ken},
  Booktitle                = {IEEE ICRA},
  Year                     = {2012},
  Pages                    = {3671--3678},

  Timestamp                = {2016.01.28}
}

@InCollection{B14,
  Title                    = {{Robot Programming by Demonstration}},
  Author                   = {Billard, Aude and Calinon, Sylvain and Dillmann, Ruediger and Schaal, Stefan},
  Booktitle                = {{Springer Handbook of Robotics}},
  Publisher                = {Springer},
  Year                     = {2008},
  Pages                    = {1371--1394},

  Timestamp                = {2016.10.25}
}

@Article{B5,
  Title                    = {{Discriminative and Adaptive Imitation in Uni-Manual and Bi-Manual Tasks}},
  Author                   = {Billard, A.G. and Calinon, S. and Guenter, F.},
  Journal                  = {{Robotics and Autonomous Systems}},
  Year                     = {2006},
  Number                   = {5},
  Pages                    = {370--384},
  Volume                   = {54},

  Timestamp                = {2008.10.17}
}

@Book{B6,
  Title                    = {Pattern Recognition and Machine Learning},
  Author                   = {Christopher M. Bishop},
  Publisher                = {Springer},
  Year                     = {2006},

  Timestamp                = {2009.04.12}
}

@InProceedings{B12,
  Title                    = {Spoken dialog challenge 2010: Comparison of live and control test results},
  Author                   = {Black, Alan W and Burger, Susanne and Conkie, Alistair and Hastie, Helen and Keizer, Simon and Lemon, Oliver and Merigaud, Nicolas and Parent, Gabriel and Schubiner, Gabriel and Thomson, Blaise and others},
  Booktitle                = {Proceedings of the SIGDIAL 2011 Conference},
  Year                     = {2011},
  Pages                    = {2--7}
}

@InProceedings{B11,
  Title                    = {{Towards Autonomous Robotic Butlers: Lessons Learned with the PR2}},
  Author                   = {Bohren, Jonathan and Rusu, Radu and Jones, E and Marder-Eppstein, Eitan and Pantofaru, Caroline and Wise, Melonee and Mosenlechner, L and Meeussen, Wim and Holzer, Stefan},
  Booktitle                = {Proceedings of ICRA},
  Year                     = {2011},
  Pages                    = {5568--5575},

  Owner                    = {sugiura},
  Timestamp                = {2013.02.25}
}

@InProceedings{B7,
  Title                    = {{Online supervised learning of non-understanding recovery policies}},
  Author                   = {Bohus, D. and Langner, B. and Raux, A. and Black, AW and Eskenazi, M. and Rudnicky, A.},
  Booktitle                = {Proceedings of the IEEE/ACL Workshop on Spoken Language Technology},
  Year                     = {2006},
  Pages                    = {170--173},

  Timestamp                = {2009.06.25}
}

@InProceedings{B9,
  Title                    = {{Sorry, I didn't catch that!-an investigation of non-understanding errors and recovery strategies}},
  Author                   = {Bohus, D. and Rudnicky, A.I.},
  Booktitle                = {Proceedings of 6th SIGdial Workshop on Discourse and Dialogue},
  Year                     = {2005},

  Timestamp                = {2009.12.17}
}

@InProceedings{B2,
  Title                    = {Evolving Complete Agents using Artificial Ontogeny},
  Author                   = {Josh C. Bongard and Rolf Pfeifer},
  Booktitle                = {Morpho-functional Machines: The New Species (Designing Embodied Intelligence)},
  Year                     = {2003},
  Editor                   = {Hara, F. and Pfeifer, R.},
  Pages                    = {237-258},
  Publisher                = {Springer-Verlag},

  Owner                    = {sugiura},
  Review                   = {遺伝子表現がやや複雑 development phase のあとタスク，さらにdevelopmentがある development phaseでは形態と神経系が発達 形態はモータ，センサを含む シミュレーションのみの研究},
  Timestamp                = {2006.06.24}
}

@Book{Braitenberg84,
  Title                    = {Vehicles: Experiments in Synthetic Psychology},
  Author                   = {Valentino Braitenberg},
  Publisher                = {MIT Press},
  Year                     = {1984},

  Address                  = {Cambridge, MA.}
}

@Article{B10,
  Title                    = {{Emotive Qualities in Lip-Synchronized Robot Speech}},
  Author                   = {Breazeal, C.},
  Journal                  = {Advanced Robotics},
  Year                     = {2003},
  Number                   = {2},
  Pages                    = {97--113},
  Volume                   = {17},

  Owner                    = {sugiura},
  Timestamp                = {2013.01.15}
}

@Article{B1,
  Title                    = {{Robots That Imitate Humans}},
  Author                   = {C. Breazeal and B. Scassellati},
  Journal                  = {{Trends in Cognitive Science}},
  Year                     = {2002},
  Pages                    = {481-487},
  Volume                   = {6},

  Abstract                 = {The study of social learning in robotics has been motivated by both scientific interest in the learning process and practical desires to produce machines that are useful, flexible, and easy to use. In this review, we introduce the social and task-oriented aspects of robot imitation. We focus on methodologies for addressing two fundamental problems. First, how does the robot know what to imitate? And second, how does the robot map that perception onto its own action repertoire to replicate it? In the future, programming humanoid robots to perform new tasks might be as simple as showing them.},
  Owner                    = {sugiura},
  Review                   = {social learning, imitationの論文に関するreview． determine which are relevant to the task, which are part of the instructional process, adn which are circumstarntial. learning from demonstration has been reviewed in detail by Schaal. information flows only in one direction. between robots with similar morphology bu which differ in scale. see [15] for a discussion of the difficulties that arsise when body structures are radically different.},
  Timestamp                = {2006.05.30}
}

@InProceedings{B4,
  Title                    = {Intelligence Without Reason},
  Author                   = {Rodney A. Brooks},
  Booktitle                = {Proceedings of the 12th International Joint Conference on Artificial Intelligence ({IJCAI}-91)},
  Year                     = {1991},
  Editor                   = {John Myopoulos and Ray Reiter},
  Pages                    = {569--595},
  Publisher                = {Morgan Kaufmann publishers Inc.: San Mateo, CA, USA},

  ISBN                     = {1-55860-160-0},
  Owner                    = {sugiura},
  Timestamp                = {2007.01.31}
}

@Article{Brooks86,
  Title                    = {A Robust Layered Control System for a Mobile Robot},
  Author                   = {Rodney A. Brooks},
  Journal                  = {IEEE Journal of Robotics and Automation},
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  Pages                    = {14-23},
  Volume                   = {RA-2}
}

@InProceedings{Buason03,
  Title                    = {Exploration of Task-Dependent Visual Morphologies in Competitive Co-evolutionary Experiments},
  Author                   = {Gunner Buason and Tom Ziemke},
  Booktitle                = {Advances in Artificial Life: Proceedings of the 7th European Conference on Artificial Life},
  Year                     = {2003},
  Editor                   = {W. Banzhaf and T. Christaller and P. Dittrich and J. T. Kim and J. Ziegler},
  Pages                    = {763-770},
  Publisher                = {Springer Verlag}
}

@InProceedings{Bugajska02,
  Title                    = {Coevolution of Form and Function in the Design of Micro Air Vehicles},
  Author                   = {Magdalena Bugajska and Alan Schultz},
  Booktitle                = {4th NASA / DoD Workshop on Evolvable Hardware (EH 2002)},
  Year                     = {2002},
  Pages                    = {154-166},
  Publisher                = {IEEE Computer Society}
}

@Article{B3,
  Title                    = {{GPS-less Low Cost Outdoor Localization For Very Small Devices}},
  Author                   = {Nirupama Bulusu and John Heidemann and Deborah Estrin},
  Journal                  = {{IEEE} Personal Communications Magazine},
  Year                     = {2000},
  Number                   = {5},
  Pages                    = {28--34},
  Volume                   = {7}
}

@Article{C13,
  Title                    = {{A Tutorial on Task-Parameterized Movement Learning and Retrieval}},
  Author                   = {Calinon, Sylvain},
  Journal                  = {{Intelligent Service Robotics}},
  Year                     = {2016},
  Number                   = {1},
  Pages                    = {1--29},
  Volume                   = {9},

  Publisher                = {Springer},
  Timestamp                = {2016.10.20}
}

@InProceedings{C4,
  Title                    = {{A Probabilistic Programming by Demonstration Framework Handling Constraints in Joint Space and Task Space}},
  Author                   = {Sylvain Calinon and Aude Billard},
  Booktitle                = {Proc. of IEEE/RSJ IROS},
  Year                     = {2008},
  Pages                    = {367-372},

  Timestamp                = {2008.10.03}
}

@InProceedings{C3,
  Title                    = {{Incremental Learning of Gestures by Imitation in a Humanoid Robot}},
  Author                   = {Sylvain Calinon and Aude Billard},
  Booktitle                = {Proc. ACM/IEEE HRI},
  Year                     = {2007},
  Pages                    = {255--262},

  Timestamp                = {2008.02.23}
}

@Article{C11,
  Title                    = {{Learning and Reproduction of Gestures by Imitation}},
  Author                   = {Calinon, Sylvain and D'halluin, Florent and Sauser, Eric L and Caldwell, Darwin G and Billard, Aude G},
  Journal                  = {IEEE Robotics \& Automation Magazine},
  Year                     = {2010},
  Number                   = {2},
  Pages                    = {44--54},
  Volume                   = {17},

  Timestamp                = {2014.03.11}
}

@InProceedings{C6,
  Title                    = {{An empirical study of the influence of argument conciseness on argument effectiveness}},
  Author                   = {Carenini, G. and Moore, J.D.},
  Booktitle                = {Proceedings of the 38th Annual Meeting on Association for Computational Linguistics},
  Year                     = {2000},
  Pages                    = {150--157},

  Timestamp                = {2010.07.08}
}

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  Title                    = {Specialist and Generalist Strategies in Sensory Evolution},
  Author                   = {Peter A. Cariani},
  Journal                  = {Artificial Life},
  Year                     = {2001},
  Pages                    = {211-214},
  Volume                   = {7},

  Owner                    = {sugiura},
  Timestamp                = {2006.11.01}
}

@Article{C5,
  Title                    = {Frames of reference in vision and language: where is above?},
  Author                   = {Laura A. Carlson-Radvansky and David E Irwin},
  Journal                  = {Cognition},
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  Number                   = {3},
  Pages                    = {223--244},
  Volume                   = {46},

  Timestamp                = {2008.10.30}
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  Title                    = {Report on the 10th IWSLT Evaluation Campaign},
  Author                   = {Cettolo, Mauro and Niehues, Jan and St{\"u}ker, Sebastian and Bentivogli, Luisa and Federico, Marcello},
  Booktitle                = {Proceedings of the International Workshop on Spoken Language Translation},
  Year                     = {2013},

  Timestamp                = {2014.02.28}
}

@InProceedings{C7,
  Title                    = {Estimation of window coefficients for dynamic feature extraction for HMM-based speech synthesis},
  Author                   = {Chen, L.H. and Nankaku, Y. and Zen, H. and Tokuda, K. and Ling, Z.H. and Dai, L.R.},
  Booktitle                = {Proc. Interspeech},
  Year                     = {2011},
  Pages                    = {1801-1804},

  Owner                    = {sugiura},
  Timestamp                = {2012.07.22}
}

@InProceedings{C2,
  Title                    = {Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion},
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  Year                     = {1998},
  Pages                    = {127--132},

  Owner                    = {sugiura},
  Review                   = {BICでspeaker diarization．かなりはっきりとピークがでる．},
  Timestamp                = {2007.07.31}
}

@Book{C8,
  Title                    = {{Tapworthy: Designing Great iPhone Apps}},
  Author                   = {Josh Clark},
  Publisher                = {O'Reilly Media},
  Year                     = {2010},

  Owner                    = {sugiura},
  Timestamp                = {2013.11.15}
}

@InProceedings{Cliff96,
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  Address                  = {Cambridge, MA.},
  Editor                   = {P. Maes and M. J. Mataric and J-A. Meyer and J. B. Pollack and S. W. Wilson},
  Pages                    = {506--515},
  Publisher                = {MIT Press}
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@InProceedings{Cliff95,
  Title                    = {Tracking the Red Queen: Measurements of adaptive progress in co-evolutionary simulations},
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  Year                     = {1995},

  Address                  = {Berlin},
  Editor                   = {F. Mor{\'a}n and A. Moreno and J. J. Merelo and P. Chac{\'o}n},
  Pages                    = {200-218},
  Publisher                = {Springer Verlag}
}

@Book{C10,
  Title                    = {The New Digital Age: Reshaping the Future of People, Nations and Business},
  Author                   = {Jared Cohen and Eric Schmidt},
  Publisher                = {John Murray},
  Year                     = {2013},

  Timestamp                = {2014.03.08}
}

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  Title                    = {Intelligibilityenhancing speech modifications: the Hurricane Challenge},
  Author                   = {Cooke, Martin and Mayo, Catherine and Valentini-Botinhao, Cassia},
  Year                     = {2013},

  Journal                  = {Proc. Interspeech},
  Timestamp                = {2014.03.24}
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@Article{D3,
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  Author                   = {Dale, R. and Reiter, E.},
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  Pages                    = {233--263},
  Volume                   = {19},

  Timestamp                = {2009.03.02}
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@Book{Damasio99,
  Title                    = {The Feeling of What Happens: Body and Emotion in the Making of Consciousness},
  Author                   = {Antonio R. Damasio},
  Publisher                = {Harcourt},
  Year                     = {1999},

  Owner                    = {sugiura},
  Timestamp                = {2007.01.31}
}

@Article{D1,
  Title                    = {From embodied to socially embedded agents -- Implications for interaction-aware robots},
  Author                   = {Kerstin Dautenhahn and Bernard Ogden and Tom Quick},
  Journal                  = {Cognitive Systems Research},
  Year                     = {2002},
  Number                   = {3},
  Pages                    = {397--428},
  Volume                   = {3},

  Owner                    = {sugiura},
  Timestamp                = {2007.02.23}
}

@Article{Dautenhahn01,
  Title                    = {Special Issue on Sensor Evolution},
  Author                   = {Kerstin Dautenhahn and Daniel Polani and Thomas Uthmann},
  Journal                  = {Artificial Life},
  Year                     = {2001},
  Number                   = {2},
  Pages                    = {95-98},
  Volume                   = {7}
}

@Article{D2,
  Title                    = {Maximum Likelihood from Incomplete Data via the {EM} Algorithm},
  Author                   = {A. P. Dempster and N. M. Laird and D. B. Rubin},
  Journal                  = {Journal of the Royal Statistical Society Series B},
  Year                     = {1977},
  Number                   = {1},
  Pages                    = {1--38},
  Volume                   = {39},

  Owner                    = {sugiura},
  Timestamp                = {2007.09.11}
}

@InCollection{Dennett84,
  Title                    = {Cognitive wheels: The frame problem of AI},
  Author                   = {Daniel C. Dennett},
  Booktitle                = {Minds, Machines and Evolution: Philosophical Studies},
  Publisher                = {Cambridge University Press},
  Year                     = {1984},

  Address                  = {Cambridge},
  Editor                   = {C. Hookaway},
  Pages                    = {129-151}
}

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@InProceedings{D4,
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@Book{Dusenbery92,
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@Book{Edelman88,
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  Title                    = {Competitive Co-Evolutionary Robotics: From Theory to Practice},
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@Article{F4,
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  Timestamp                = {2012.07.22}
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@Book{FRIEDL97,
  Title                    = {Mastering Regular Expressions},
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  Title                    = {{Sequential Non-Stationary Noise Tracking Using Particle Filtering with Switching Dynamical System}},
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  Owner                    = {sugiura},
  Review                   = {LEGOのブロックをGAで形成する 形だけ． クレーンなどを作る},
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  ISBN                     = {0395270499},
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@InProceedings{G5,
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  Pages                    = {371-377},
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  Review                   = {ベイズネットで因果関係を研究している著者のレビュー 以下の研究成果がある 8か月で人工言語からsyllableを学習できる 画像情報から時系列を学習できる goal-directedな行為に分割できる 18か月で他人の失敗した行為を補完できる 3か月の子供に両面テープグローブをつける(そうしないと物がつかめない)と，他人のリーチングを理解できるようになる 「なぜパブロフの犬は腹が減ったときにベルを鳴らさなかったのか？」 k.s.オペラント条件付け 「ベルを鳴らす->飯が来る」の因果関係は学習してない},
  Timestamp                = {2006.06.21}
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@InProceedings{H2,
  Title                    = {Learning of the reference-point-dependent concepts on movement for language acquisition},
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@Article{H7,
  Title                    = {The Symbol Grounding Problem},
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  Journal                  = {Physica D},
  Year                     = {1990},
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@Article{H13,
  Title                    = {{Review of Sensor Placement Strategies for Contamination Warning Systems in Drinking Water Distribution Systems}},
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  Year                     = {2010},
  Number                   = {6},
  Pages                    = {611--619},
  Volume                   = {136},

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@Book{H9,
  Title                    = {A Hybrid Deliberative Layer for Robotic Agents},
  Author                   = {Ronny Hartanto},
  Publisher                = {Springer},
  Year                     = {2011},

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  Timestamp                = {2013.02.26}
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@InProceedings{H14,
  Title                    = {{A Field Study of the Human Support Robot in the Home Environment}},
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@TechReport{H11,
  Title                    = {{Creating Cognitive Applications Powered by IBM Watson: Getting started with the API}},
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@InProceedings{H15,
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  Volume                   = {26},

  Owner                    = {sugiura},
  Publisher                = {Springer},
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@InProceedings{Hillis92,
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  Address                  = {Redwood City, CA},
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@Article{H10,
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@InProceedings{H5,
  Title                    = {{Statistical dialog management applied to WFST-based dialog systems}},
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  Year                     = {2009},
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@Article{Hosoda04E,
  Title                    = {What Morphology Brings to Learning, What Learning Brings to Morphology},
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  Note                     = {(in Japanese)},
  Number                   = {2},
  Pages                    = {186-189},
  Volume                   = {22}
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@Misc{H4,
  Title                    = {{The Hidden Markov Model Toolkit}},

  Author                   = {{HTK Web Site}},
  HowPublished             = {http://htk.eng.cam.ac.uk/},

  Timestamp                = {2008.10.08}
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@Misc{K8,
  Author                   = {http://kitchen.cs.cmu.edu/},

  Timestamp                = {2008.10.29}
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@Misc{S11,
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  Timestamp                = {2008.10.08}
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@InProceedings{H12,
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@Article{I10,
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@Article{I3,
  Title                    = {{Embodied Symbol Emergence Based on Mimesis Theory}},
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  Journal                  = {{International Journal of Robotics Research}},
  Year                     = {2004},
  Number                   = {4},
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  Owner                    = {sugiura},
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@InProceedings{I1,
  Title                    = {Acceleration of reinforcement learning by a mobile robot usinggeneralized rules},
  Author                   = {Kousuke Inoue and Jun Ota and Tomohiko Katayama and Tamio Arai},
  Booktitle                = {Proceedings of 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000)},
  Year                     = {2000},
  Pages                    = {885-890},

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  Timestamp                = {2007.01.31}
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@Article{I15,
  Title                    = {{RoboCup@Home: Analysis and Results of Evolving Competitions for Domestic and Service Robots}},
  Author                   = {Iocchi, Luca and Holz, Dirk and Ruiz-del-Solar, Javier and Sugiura, Komei and van der Zant, Tijn},
  Journal                  = {{Artificial Intelligence}},
  Year                     = {2015},
  Pages                    = {258--281},
  Volume                   = {229},

  Timestamp                = {2015.12.21}
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@InProceedings{I8,
  Title                    = {{RoboCup@Home: Adaptive Benchmarking of Robot Bodies and Minds}},
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  Booktitle                = {{Proceedings of the International Conference on Simulation, Modeling and Programming for Autonomous Robots}},
  Year                     = {2010},
  Pages                    = {171--182},

  Owner                    = {sugiura},
  Timestamp                = {2012.01.11}
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@InProceedings{I16,
  Title                    = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
  Author                   = {Ioffe, Sergey and Szegedy, Christian},
  Booktitle                = {{Proc. ICML}},
  Year                     = {2015},
  Pages                    = {448--456},

  Timestamp                = {2017.07.09}
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@Article{I4,
  Title                    = {Robust Speech Recognition System for Communication Robots in Real Environments},
  Author                   = {Ishi, CT and Matsuda, S. and Kanda, T. and Jitsuhiro, T. and Ishiguro, H. and Nakamura, S. and Hagita, N.},
  Journal                  = {{IEEE Transaction on Robotics}},
  Year                     = {2008},
  Number                   = {3},
  Pages                    = {759--763},
  Volume                   = {24},

  Timestamp                = {2008.07.04}
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@InProceedings{I13,
  Title                    = {Analysis of Acoustic-Prosodic Features Related to Paralinguistic Information Carried by Interjections in Dialogue Speech},
  Author                   = {Ishi, Carlos T and Ishiguro, Hiroshi and Hagita, Norihiro},
  Booktitle                = {Proc. {INTERSPEECH}},
  Year                     = {2011},

  Owner                    = {sugiura},
  Timestamp                = {2013.03.23}
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@InCollection{I2,
  Title                    = {Robots That Learn Language: Developmental Approach to Human-Machine Conversations},
  Author                   = {Naoto Iwahashi},
  Booktitle                = {Human-Robot Interaction},
  Publisher                = {I-Tech Education and Publishing},
  Year                     = {2007},
  Editor                   = {Sanker, N.},
  Pages                    = {95-118},

  Owner                    = {sugiura},
  Timestamp                = {2007.08.30}
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@Article{I6,
  Title                    = {Interactive Learning of Spoken Words and Their Meanings through an Audio-Visual Interface},
  Author                   = {Iwahashi, N.},
  Journal                  = {{IEICE} Transactions on information and systems},
  Year                     = {2008},
  Number                   = {2},
  Pages                    = {312},
  Volume                   = {91},

  Timestamp                = {2009.02.16}
}

@InProceedings{I7,
  Title                    = {{Robots that Learn to Converse: Developmental Approach to Situated Language Processing}},
  Author                   = {Naoto Iwahashi and Ryo Taguchi and Komei Sugiura and Kotaro Funakoshi and Mikio Nakano},
  Booktitle                = {Proceedings of International Symposium on Speech and Language Processing},
  Year                     = {2009},
  Pages                    = {532--537},

  Timestamp                = {2009.12.17}
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@PhdThesis{Iwataphdthesis,
  Title                    = {An Information Theoretic Analysis of Reinforcement Learning},
  Author                   = {Kazunori Iwata},
  School                   = {Kyoto University},
  Year                     = {2005},

  Owner                    = {sugiura},
  Timestamp                = {2007.01.31}
}

@InProceedings{I11,
  Title                    = {{Conversational Speech Synthesis System with Communication Situation Dependent HMMs}},
  Author                   = {Iwata, K. and Kobayashi, T.},
  Booktitle                = {{Proc. of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop}},
  Year                     = {2011},
  Pages                    = {113--123},

  Owner                    = {sugiura},
  Timestamp                = {2013.01.29}
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@Article{J1,
  Title                    = {Emergence of Trichromatic Color Vision in Mice Engineered to Express a Human Cone Photopigment},
  Author                   = {Gerald H. Jacobs and others},
  Journal                  = {Science},
  Year                     = {2007},
  Pages                    = {1723-1725},
  Volume                   = {315},

  Abstract                 = {ヒトは世界をフルカラーで見ることができるが、他のほ乳類はずっと限られたスペクトルしか認識できない。今回新たに作成されたフルカラーの視覚を持った遺伝子操作マウスの系統は、どのようにして我々の先祖が、遺伝子の突然変異によって虹の5色をみることができるようになったか、明らかにしてくれるだろう。ほとんどのほ乳類は二色型色覚（ヒトでいえば、黄色、青色、灰色を認識することができる）であるが、霊長類は三色型色覚でものを見ている。三色型色覚には光色素と呼ばれる光感受性の分子が3種類と適切な神経接続が必要である。新たな研究から、霊長類で遺伝子の変化により光色素が追加されると、脳は新しい信号を処理しフルカラーの視覚を獲得するよう自分で「回路を再構築」することができる、ということがわかった。 Gerald Jacobsらは、マウスの系統のX染色体上に突然変異を起こし、マウスが光の長い波長を認識できる3つ目の光色素を作るようにした。色の異なるパネルや光を区別する作業をマウスに実行させ、マウスがフルカラーの視覚を獲得していることを確認した。},
  Owner                    = {sugiura},
  Timestamp                = {2007.03.24}
}

@InProceedings{J8,
  Title                    = {{Learning Trajectory Preferences for Manipulators via Iterative Improvement}},
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  Booktitle                = {{Advances in Neural Information Processing Systems}},
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@Book{Maturana87,
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  Owner                    = {sugiura},
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@Book{Maturana80,
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  Title                    = {Some Philosophical Problems from the Standpoint of Artificial Intelligence},
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  Year                     = {2007},
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  Year                     = {2012},
  Pages                    = {187-204},
  Volume                   = {66},

  Owner                    = {sugiura},
  Publisher                = {Springer},
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  Year                     = {2014},
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  Booktitle                = {Imitation in Animals and Artifacts},
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  Year                     = {2002},
  Pages                    = {41-61},

  Timestamp                = {2008.10.28}
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  Title                    = {The Robot in the Swarm: An Investigation into Agent Embodiment within Virtual Robotic Swarms},
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  Review                   = {相関係数，information metric, KL divergence, Bhattacharya distanceをカメラ入力で比較． Information metricが一番の結果らしい information metricは条件付きエントロピーの平均で定義される},
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  Title                    = {{RoboBrain: Large-Scale Knowledge Engine for Robots}},

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  Owner                    = {sugiura},
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  Author                   = {van der Sluis, I. and Gatt, A. and van Deemter, K.},
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  Year                     = {2006},

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  Title                    = {{Estimating Uncertain Spatial Relationships in Robotics}},
  Author                   = {Randall Smith and Matthew Self and Peter Cheeseman},
  Booktitle                = {Autonomous Robot Vehicules},
  Publisher                = {Springer-Verlag New York, Inc.},
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  Book                     = {Autonomous robot vehicles},
  Review                   = {kalman filterを用いたmappingの解説． これでkalman filterを覚えた}
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  Title                    = {{RoboCup@Home: Demonstrating Everyday Manipulation Skills in RoboCup@Home}},
  Author                   = {St\"{u}ckler, J. and Holz, D. and Behnke, S.},
  Journal                  = {Robotics \& Automation Magazine, IEEE},
  Year                     = {2012},
  Number                   = {2},
  Pages                    = {34--42},
  Volume                   = {19},

  Owner                    = {sugiura},
  Timestamp                = {2013.01.29}
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@Article{S18,
  Title                    = {{Efficient 3D Object Perception and Grasp Planning for Mobile Manipulation in Domestic Environments}},
  Author                   = {St\"{u}ckler, J. and Steffens, R. and Holz, D. and Behnke, S.},
  Journal                  = {Robotics and Autonomous Systems},
  Year                     = {2012},

  Owner                    = {sugiura},
  Timestamp                = {2013.02.25}
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@Article{S8,
  Title                    = {Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus},
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  Year                     = {1999},
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  Volume                   = {19},

  Owner                    = {sugiura},
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@TechReport{S3,
  Title                    = {Computational Model for an Extendable Robot Body Schema},
  Author                   = {Alexander Stoytchev},
  Institution              = {Georgia Institute of Technology, College of Computing},
  Year                     = {2003},
  Type                     = {Technical Report GIT-CC-03-44},

  Owner                    = {sugiura},
  Timestamp                = {2006.07.15}
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  Title                    = {{Report on the {\it Second} Second Challenge on Generating Instructions in Virtual Environments (GIVE-2.5)}},
  Author                   = {Striegnitz, Kristina and Denis, Alexandre and Gargett, Andrew and Garoufi, Konstantina and Koller, Alexander and Theune, Mari{\"e}t},
  Booktitle                = {Proceedings of the 13th European Workshop on Natural Language Generation},
  Year                     = {2011},
  Pages                    = {270--279},

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}

@Article{S1,
  Title                    = {{Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes}},
  Author                   = {Yuuya Sugita and Jun Tani},
  Journal                  = {Adaptive Behavior},
  Year                     = {2005},
  Number                   = {1},
  Pages                    = {33--52},
  Volume                   = {13},
  Owner                    = {sugiura},
  Review                   = {RNNPBで2語文と行動を認識および生成させる 2語文でRNNがいるのか？ 順番を変えると動かなくなるのは凡化と言えるのか？ HMMの間の類似性をKLで測るのと同じ系統},
  Timestamp                = {2006.06.23}
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@InProceedings{Sugiura08iros,
  Title                    = {{Motion Recognition and Generation by Combining Reference-Point-Dependent Probabilistic Models}},
  Author                   = {Sugiura, K. and Iwahashi, N.},
  Booktitle                = {{Proc. IEEE/RSJ IROS}},
  Year                     = {2008},
  Pages                    = {852--857},

  Timestamp                = {2009.02.25}
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@InProceedings{Sugiura07iwmisi,
  Title                    = {Learning object-manipulation verbs for human-robot communication},
  Author                   = {Komei Sugiura and Naoto Iwahashi},
  Booktitle                = {Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction},
  Year                     = {2007},
  Pages                    = {32--38},

  Timestamp                = {2008.02.23}
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@InProceedings{Sugiura11iros,
  Title                    = {{Motion Generation by Reference-Point-Dependent Trajectory HMMs}},
  Author                   = {Sugiura, K. and Iwahashi, N. and Kashioka, H.},
  Booktitle                = {{Proc. IEEE/RSJ IROS}},
  Year                     = {2011},
  Pages                    = {350--356},

  Owner                    = {sugiura},
  Timestamp                = {2012.07.22}
}

@Article{Sugiura11ersja,
  Title                    = {{Learning, Generation, and Recognition of Motions by Reference-Point-Dependent Probabilistic Models}},
  Author                   = {Komei Sugiura and Naoto Iwahashi and Hideki Kashioka and Satoshi Nakamura},
  Journal                  = {Advanced Robotics},
  Year                     = {2011},
  Number                   = {6-7},
  Pages                    = {825-848},
  Volume                   = {25},

  Owner                    = {sugiura},
  Timestamp                = {2010.12.14}
}

@InProceedings{Sugiura09interspeech,
  author    = {Komei Sugiura and Naoto Iwahashi and Hideki Kashioka and Satoshi Nakamura},
  title     = {{Bayesian Learning of Confidence Measure Function for Generation of Utterances and Motions in Object Manipulation Dialogue Task}},
  booktitle = {Proc. Interspeech},
  year      = {2009},
  pages     = {2483-2486},
  timestamp = {2010.03.11},
}

@Article{Sugiura11ersjb,
  Title                    = {{Situated Spoken Dialogue with Robots Using Active Learning}},
  Author                   = {Sugiura, K. and Iwahashi, N. and Kawai, H. and Nakamura, S.},
  Journal                  = {Advanced Robotics},
  Year                     = {2011},
  Number                   = {17},
  Pages                    = {2207--2232},
  Volume                   = {25},

  Owner                    = {sugiura},
  Timestamp                = {2012.08.04}
}

@Conference{Sugiura10dwr,
  Title                    = {{Active Learning for Generating Motion and Utterances in Object Manipulation Dialogue Tasks}},
  Author                   = {Sugiura, K. and Iwahashi, N. and Kawai, H. and Nakamura, S.},
  Booktitle                = {2010 AAAI Fall Symposium Series},
  Year                     = {2010},
  Pages                    = {115-120},

  Owner                    = {sugiura},
  Timestamp                = {2010.12.08}
}

@Conference{Sugiura10iros,
  Title                    = {{Active Learning of Confidence Measure Function in Robot Language Acquisition Framework}},
  Author                   = {Sugiura, K. and Iwahashi, N. and Kawai, H. and Nakamura, S.},
  Booktitle                = {Proceedings of IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010)},
  Year                     = {2010},
  Pages                    = {1774-1779},

  Owner                    = {sugiura},
  Timestamp                = {2010.12.08}
}

@InProceedings{Sugiura06sice,
  Title                    = {Construction of Robotic Body Schema by Extracting Temporal Information from Sensory Inputs},
  Author                   = {Komei Sugiura and Daisuke Matsubara and Osamu Katai},
  Booktitle                = {Proceedings of SICE-ICASE International Joint Conference 2006},
  Year                     = {2006},

  Address                  = {Busan, Korea},
  Month                    = {October},
  Pages                    = {to appear},

  File                     = {sugiura06sice.pdf:/home/sugiura/public_html/publications/sugiura06sice.pdf:PDF},
  Owner                    = {sugiura},
  Timestamp                = {2006.07.18}
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@Article{sugiura15tadr,
  Title                    = {{A Cloud Robotics Approach towards Dialogue-Oriented Robot Speech}},
  Author                   = {Komei Sugiura and Yoshinori Shiga and Hisashi Kawai and Teruhisa Misu and Chiori Hori},
  Journal                  = {Advanced Robotics},
  Year                     = {2015},
  Number                   = {7},
  Pages                    = {449-456},
  Volume                   = {29},

  Booktitle                = {Advanced Robotics},
  Owner                    = {sugiura},
  Timestamp                = {2014.01.28}
}

@InProceedings{sugiura14icra,
  Title                    = {{Non-Monologue HMM-Based Speech Synthesis for Service Robots: A Cloud Robotics Approach}},
  Author                   = {Komei Sugiura and Yoshinori Shiga and Hisashi Kawai and Teruhisa Misu and Chiori Hori},
  Booktitle                = {Proc. {ICRA}},
  Year                     = {2014},
  Pages                    = {2237-2242},

  Owner                    = {sugiura},
  Timestamp                = {2014.01.28}
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@Article{Sugiura04ieej,
  Title                    = {Co-evolution of Sensors and Controllers},
  Author                   = {Komei Sugiura and Takayuki Shiose and Hiroshi Kawakami and Osamu Katai},
  Journal                  = {IEEJ Trans. EIS},
  Year                     = {2004},
  Number                   = {10},
  Pages                    = {1938-1943},
  Volume                   = {124-C}
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@InProceedings{Sugiura03ies,
  Title                    = {Co-evolution of Sensors and Controllers},
  Author                   = {Komei Sugiura and Takayuki Shiose and Hiroshi Kawakami and Osamu Katai},
  Booktitle                = {Proceedings of the 7th Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems},
  Year                     = {2003},

  Address                  = {Kitakyushu, Japan},
  Month                    = {November},
  Pages                    = {145-150},

  Owner                    = {sugiura},
  Timestamp                = {2006.07.18}
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@InProceedings{Sugiura05smc,
  Title                    = {Exploiting Interaction between Sensory Morphology and Learning},
  Author                   = {Komei Sugiura and Takayuki Shiose and Katsunori Shimohara and Osamu Katai},
  Booktitle                = {Proceedings of 2005 IEEE Intl. Conf. on Systems, Man and Cybernetics (SMC 2005)},
  Year                     = {2005},

  Address                  = {Hawaii, USA},
  Month                    = {October},
  Pages                    = {883-888},

  Owner                    = {sugiura},
  Timestamp                = {2006.07.18}
}

@InProceedings{Sugiura03ecal,
  Title                    = {Evolution of Rewriting Rule Sets Using String-Based Tierra},
  Author                   = {Komei Sugiura and Hideaki Suzuki and Takayuki Shiose and Hiroshi Kawakami and Osamu Katai},
  Booktitle                = {Advances in Artificial Life: Proceedings of the 7th European Conference on Artificial Life},
  Year                     = {2003},

  Address                  = {Dortmund, Germany},
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  Month                    = {September},
  Pages                    = {69-77},
  Publisher                = {Springer Verlag}
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@InProceedings{sugiura15iros,
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  Author                   = {Komei Sugiura and Koji Zettsu},
  Booktitle                = {Proc. {IEEE/RSJ IROS}},
  Year                     = {2015},
  Pages                    = {6155-6160},

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  Journal                  = {{Advanced Robotics}},
  Year                     = {2011},
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  Volume                   = {25},

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  Publisher                = {Springer},
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  Journal                  = {New Frontiers in Artificial Intelligence},
  Owner                    = {sugiura},
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  Booktitle                = {Proc. IEEE International Congress on Big Data},
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@Article{T7,
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  Author                   = {Tadahiro Taniguchi and Naoto Iwahashi and Komei Sugiura and Tetsuo Sawaragi},
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  Year                     = {2008},
  Number                   = {4},
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  Journal                  = {Advanced Robotics},
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@Comment{jabref-meta: databaseType:bibtex;}

@Comment{jabref-meta: selector_journal:Adaptive Behavior;Artificial Intelligence;Artificial Life;Science;}

@Comment{jabref-meta: selector_publisher:MIT Press;Springer-Verlag;}
