Interpretable Active Inference Gait Control Learning

Abstract

Sustaining the gait locomotion in an adversarial environment requires the robot to react to novel experiences adaptively. In Free Energy Principle (FEP), the behavioral reaction is driven by the discrepancy between observation and prediction. Although, for legged robot gait locomotion, the prediction of gait dynamics is challenging as the consequences non-linearly depend on the activity history, the animal gait is robust, adapting to severe motion disruptions seemingly instantly. In biomimetic robotics, the Central Pattern Generator (CPG) relaxes the general dynamics of body-environment interaction to the stable and repetitive dynamics of gait. Based on these observations, we propose self-learning of the gait dynamics model and FEP framework that infers state estimation and gait control. The proposed method is experimentally evaluated on a real hexapod walking robot with 18 controllable degrees of freedom. The robot learns the gait dynamics model indoors and then deploys it in outdoor navigation under various adversarial scenarios. Results show that the developed interpretable gait controller exhibits complex and real-time adaptive behavior when it encounters unknown situations.