  Humans learn about objects via interaction and using multiple perceptions,
such as vision, sound, and touch. While vision can provide information about an
object's appearance, non-visual sensors, such as audio and haptics, can provide
information about its intrinsic properties, such as weight, temperature,
hardness, and the object's sound. Using tools to interact with objects can
reveal additional object properties that are otherwise hidden (e.g., knives and
spoons can be used to examine the properties of food, including its texture and
consistency). Robots can use tools to interact with objects and gather
information about their implicit properties via non-visual sensors. However, a
robot's model for recognizing objects using a tool-mediated behavior does not
generalize to a new tool or behavior due to differing observed data
distributions. To address this challenge, we propose a framework to enable
robots to transfer implicit knowledge about granular objects across different
tools and behaviors. The proposed approach learns a shared latent space from
multiple robots' contexts produced by respective sensory data while interacting
with objects using tools. We collected a dataset using a UR5 robot that
performed 5,400 interactions using 6 tools and 6 behaviors on 15 granular
objects and tested our method on cross-tool and cross-behavioral transfer
tasks. Our results show the less experienced target robot can benefit from the
experience gained from the source robot and perform recognition on a set of
novel objects. We have released the code, datasets, and additional results:
https://github.com/gtatiya/Tool-Knowledge-Transfer.
