その場で適応：単一ライフでのロボット展開のための行動変調
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
事前学習した多様な行動の中から状況に応じて価値評価に基づき選択・適応し、テスト時の1エピソード内で人間の監督なしに動的に適応する手法ROAMを提案。実機四足歩行ロボットGo1でローラースケート装着時にも前進可能。
著者: Annie S. Chen, Govind Chada, Laura Smith, Archit Sharma, Zipeng Fu, Sergey Levine, Chelsea Finn
分類: cs.RO, cs.LG
原文アブストラクト
To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previouslylearned behaviors. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors to select and adapt pre-trained behaviors to the situation at hand. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision. We demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet. Our approach adapts over 2x as efficiently compared to existing methods when facing a variety of out-of-distribution situations during deployment by effectively choosing and adapting relevant behaviors on-the-fly.
