Leveraging Language for Accelerated Learning of Tool Manipulation

Allen Z. Ren, Bharat Govil, Tsung-Yen Yang, Karthik R Narasimhan, Anirudha Majumdar

Keywords: Language for Robotics, Tool Manipulation, Meta-learning

TL;DR: We combine language descriptions and meta-learning to accelerate tool learning in various manipulation tasks.

Abstract: Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools. We investigate whether linguistic information about a tool (e.g. its geometry, common uses) can help control policies adapt faster to new tools for a given task. We obtain diverse descriptions of various tools in natural language and use pre-trained language models to generate their feature representations. We then perform language-conditioned meta-learning to learn policies that can efficiently adapt to new tools given their corresponding text descriptions. Our results demonstrate that combining linguistic information and meta-learning significantly accelerates tool learning in several manipulation tasks including pushing, lifting, sweeping, and hammering.