Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of BehaviorDownload PDF

16 Jun 2022, 10:45 (modified: 03 Dec 2022, 14:27)CoRL 2022 OralReaders: Everyone
Student First Author: yes
Keywords: Locomotion, Reinforcement Learning, Task Specification
TL;DR: Learning quadrupedal locomotion with an expanded task specification enables online tuning of a small quadruped to out-of-distribution environments and tasks.
Abstract: 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
Supplementary Material: zip
Website: https://gmargo11.github.io/walk-these-ways/
Code: https://github.com/Improbable-AI/walk-these-ways
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