A model-free method to learn multiple skills in parallel on modular robots.

ABSTRACT: Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model and its dynamics, are necessary. In this paper, we present a generic method based on Central Pattern Generators, that enables the acquisition of basic locomotion skills in parallel, through very few trials. The novelty of our approach, underpinned by a mathematical analysis of the controller model, is to search for good initial states, instead of optimising connection weights. Empirical validation in six different robot morphologies demonstrates

SUBMITTER: van Diggelen F 

PROVIDER: S-EPMC11269725 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature