  Many robotic tasks are still teleoperated since automating them is very time
consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce
programming time and cost. However, conventional RLfD approaches are not
directly applicable to many robotic tasks, e.g. robotic suturing with minimally
invasive robots, as they require a time-consuming process of designing features
from visual information. Deep Neural Networks (DNN) have emerged as useful
tools for creating complex models capturing the relationship between
high-dimensional observation space and low-level action/state space.
Nonetheless, such approaches require a dataset suitable for training
appropriate DNN models. This paper presents a dataset of inserting/piercing a
needle with two arms of da Vinci Research Kit in/through soft tissues. The
dataset consists of (1) 60 successful needle insertion trials with randomised
desired exit points recorded by 6 high-resolution calibrated cameras, (2) the
corresponding robot data, calibration parameters and (3) the commanded robot
control input where all the collected data are synchronised. The dataset is
designed for Deep-RLfD approaches. We also implemented several deep RLfD
architectures, including simple feed-forward CNNs and different Recurrent
Convolutional Networks (RCNs). Our study indicates RCNs improve the prediction
accuracy of the model despite that the baseline feed-forward CNNs successfully
learns the relationship between the visual information and the next step
control actions of the robot. The dataset, as well as our baseline
implementations of RLfD, are publicly available for bench-marking at
https://github.com/imanlab/d-lfd.
