English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This work presents Neural Gaits, a method for learning dynamic walking gaitsthrough the enforcement of set invariance that can be refined episodicallyusing experimental data from the robot. We frame walking as a set invarianceproblem enforceable via control barrier functions (CBFs) defined on thereduced-order dynamics quantifying the underactuated component of the robot:the zero dynamics. Our approach contains two learning modules: one for learninga policy that satisfies the CBF condition, and another for learning a residualdynamics model to refine imperfections of the nominal model. Importantly,learning only over the zero dynamics significantly reduces the dimensionalityof the learning problem while using CBFs allows us to still make guarantees forthe full-order system. The method is demonstrated experimentally on anunderactuated bipedal robot, where we are able to show agile and dynamiclocomotion, even with partially unknown dynamics.

Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies