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

By Gabriel B. Margolis. Conference on Robot Learning, 2022

304 citations recorded by the source corpus.

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/

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/039cac821166586d4e9bebf1070941f3939e0d71

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, background): “For instance, MoB (52) encoded various locomotion strategies into a single policy conditioned on gait parameters.”
Foundation models in robotics: Applications, challenges, and the future (2023, unknown): “Developing techniques that perform runtime monitoring and OOD detection with statistical guarantees on false positive/negative error rates in a data-efﬁcient manner remains an important research direction.”