  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/

Robotics;Artificial Intelligence;Machine Learning;Systems and Control;Systems and Control