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

• URL: http://arxiv.org/abs/2212.03238v1
• Date: Tue, 6 Dec 2022 18:59:34 GMT
• Title: Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior
• Authors: Gabriel B Margolis and Pulkit Agrawal
• Abstract summary: We learn a single policy that encodes a structured family of locomotion strategies that solve training tasks in different ways. 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.
• Score: 12.91132798749
• License: http://creativecommons.org/licenses/by/4.0/
• 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/

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