歩き方いろいろ：行動の多様性による汎化のためのロボット制御チューニング
Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior
単一の方策で多様な歩容（姿勢・速度・足運び）を実現する四足歩行コントローラを学習し、再学習なしに新しいタスクや環境に適応できるようにした。
著者: Gabriel B Margolis, Pulkit Agrawal
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Learned locomotion policies can rapidly adapt to diverse environments similar to those experienced during training but lack a mechanism for fast tuning when they fail in an out-of-distribution test environment. This necessitates a slow and iterative cycle of reward and environment redesign to achieve good performance on a new task. As an alternative, we propose learning a single policy that encodes a structured family of locomotion strategies that solve training tasks in different ways, resulting in Multiplicity of Behavior (MoB). Different strategies generalize differently and can be chosen in real-time for new tasks or environments, bypassing the need for time-consuming retraining. We release a fast, robust open-source MoB locomotion controller, Walk These Ways, that can execute diverse gaits with variable footswing, posture, and speed, unlocking diverse downstream tasks: crouching, hopping, high-speed running, stair traversal, bracing against shoves, rhythmic dance, and more. Video and code release: https://gmargo11.github.io/walk-these-ways/
