Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning

Abstract

Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been a grand challenge for autonomous robotic systems. Although data-driven approaches using reinforcement learning can develop specialist policies that discover behaviors to control a single object, they often exhibit poor generalization to unseen ones. In this work, we show that policies learned by existing reinforcement learning algorithms can in fact be generalist when combined with multi-task learning and a well-chosen object representation. We show that a single generalist policy can perform in-hand manipulation of over 100 geometrically-diverse real-world objects and generalize to new objects with unseen shape or size. Interestingly, we find that multi-task learning with object point cloud representations not only generalizes better but even outperforms the single-object specialist policies on both training as well as held-out test objects. Video results at https://huangwl18.github.io/geometry-dex

Website at https://huangwl18.github.io/geometry-dex

References in corpus (6)

• Emergence of Locomotion Behaviours in Rich Environments
• Solving Rubik's Cube with a Robot Hand
• RL${}^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
• Learning to reinforcement learn
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
• A System for General In-Hand Object Re-Orientation