Improved Performance of CPG Parameter Inference for Path-following Control of Legged Robots

The difficulty associated with the coordinated locomotion of legged robots grows quickly as the number of joints increases. Although prior approaches have addressed this problem through sampling-based planners, learning-based techniques have recently been explored as a means to handle such complexity. Among these recent approaches are systems that utilize probabilistic graphical models in order to infer parameters for central pattern generators (CPGs) which enable the path-following locomotion of highly-articulated legged robots through unstructured terrain. This paper presents a novel formulation of a CPG parameter inference-based path-following controller. The new inference process and accompanying CPG formulation enforce oscillator convergence to the limit-cycle specified by the inferred parameters in addition to biasing towards parameters that quickly reach stable-state. This formulation is shown to improve the performance of CPG parameter inference-based path-following control for legged robots across a number of simulated and physical experiments.