Learnable Tegotae-based Feedback in CPGs with Sparse Observation Produces Efficient and Adaptive Locomotion

Abstract

Central Pattern generators (CPG) are a biologically inspired, decentralized control architecture that enables model-free, but yet adaptively stable and computational lightweight locomotion capabilities on complex robots. Nevertheless, no unified design guidelines for closed-loop CPG controllers are available in the literature. Therefore, we propose a task-distributed, end-to-end trainable, closed-loop CPG control policy by generalizing and extending Tegotae control. The Tegotae approach modulates CPG activity by quantifying the discrepancy between internal belief states and environmental reactions. Spontaneous and adaptive gait formation towards situationally efficient locomotion patterns are intrinsic properties of Tegotae control. The Tegotae control policy is trained and benchmarked in simulation on a 1D hopping robot. We found that our approach can learn efficient and adaptive locomotion on minimal feedback information, while out-performing unstructured, classic reinforcement learning policies of equal complexity. To the best of our knowledge, this is the first study to fully generalize the Tegotae approach and construct unimpeded, end-to-end trainable Tegotae control policies.