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Humans learn about objects via interaction and using multiple perceptions,such as vision, sound, and touch. While vision can provide information about anobject's appearance, non-visual sensors, such as audio and haptics, can provideinformation about its intrinsic properties, such as weight, temperature,hardness, and the object's sound. Using tools to interact with objects canreveal additional object properties that are otherwise hidden (e.g., knives andspoons can be used to examine the properties of food, including its texture andconsistency). Robots can use tools to interact with objects and gatherinformation about their implicit properties via non-visual sensors. However, arobot's model for recognizing objects using a tool-mediated behavior does notgeneralize to a new tool or behavior due to differing observed datadistributions. To address this challenge, we propose a framework to enablerobots to transfer implicit knowledge about granular objects across differenttools and behaviors. The proposed approach learns a shared latent space frommultiple robots' contexts produced by respective sensory data while interactingwith objects using tools. We collected a dataset using a UR5 robot thatperformed 5,400 interactions using 6 tools and 6 behaviors on 15 granularobjects and tested our method on cross-tool and cross-behavioral transfertasks. Our results show the less experienced target robot can benefit from theexperience gained from the source robot and perform recognition on a set ofnovel objects. We have released the code, datasets, and additional results:https://github.com/gtatiya/Tool-Knowledge-Transfer.

Cross-Tool and Cross-Behavior Perceptual Knowledge Transfer for Grounded Object Recognition