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Learned locomotion policies can rapidly adapt to diverse environments similarto those experienced during training but lack a mechanism for fast tuning whenthey fail in an out-of-distribution test environment. This necessitates a slowand iterative cycle of reward and environment redesign to achieve goodperformance on a new task. As an alternative, we propose learning a singlepolicy that encodes a structured family of locomotion strategies that solvetraining tasks in different ways, resulting in Multiplicity of Behavior (MoB).Different strategies generalize differently and can be chosen in real-time fornew tasks or environments, bypassing the need for time-consuming retraining. Werelease 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 andcode release: https://gmargo11.github.io/walk-these-ways/

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