Learning Generalizable Tool Use with Non-rigid Grasp-pose Registration
CASE• 2023
Abstract
Tool use, a hallmark feature of human intelligence, remains a challenging
problem in robotics due the complex contacts and high-dimensional action space.
In this work, we present a novel method to enable reinforcement learning of
tool use behaviors. Our approach provides a scalable way to learn the operation
of tools in a new category using only a single demonstration. To this end, we
propose a new method for generalizing grasping configurations of multi-fingered
robotic hands to novel objects. This is used to guide the policy search via
favorable initializations and a shaped reward signal. The learned policies
solve complex tool use tasks and generalize to unseen tools at test time.
Visualizations and videos of the trained policies are available at
https://maltemosbach.github.io/generalizable_tool_use.