人の実演からソフトロボットハンドの巧みな操作を学習
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration
人間が物体を動かす実演を対象中心に記録し、強化学習でソフトハンドが模倣できるよう、実現可能な実演の選択・混合とguided policy searchを拡張した手法を提案した。
著者: Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel
分類: cs.LG, cs.RO
原文アブストラクト
Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are often expensive and fragile. Low-cost soft hands offer an appealing alternative to more conventional devices, but present considerable challenges in sensing and actuation, making them difficult to apply to more complex manipulation tasks. In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks. Our method uses object-centric demonstrations, where a human demonstrates the desired motion of manipulated objects with their own hands, and the robot autonomously learns to imitate these demonstrations using reinforcement learning. We propose a novel algorithm that allows us to blend and select a subset of the most feasible demonstrations to learn to imitate on the hardware, which we use with an extension of the guided policy search framework to use multiple demonstrations to learn generalizable neural network policies. We demonstrate our approach on the RBO Hand 2, with learned motor skills for turning a valve, manipulating an abacus, and grasping.
