Lifelong Active Inference of Gait Control

Sustaining the robot's longevity becomes challenging in dynamic deployments characterized by new unknown environments and embodiments outside of the prior knowledge. Hence, the knowledge of robot-environment interactions needs to be continually updated for system adaptation. It can be implemented through self-verification as a continual comparison of predictions with observations using the predictive coding (PC) principle. The principle has been further extended into the active inference control (AIC) in biomimetic robotics to drive the control, state estimation, and model update. However, continually updating one model leads to catastrophic forgetting in the long term. Therefore, we propose an autonomously expanding self-verifying world model (WM) of sensorimotor dynamics utilized in model-based gait control. The model combines PC with the incremental knowledge representation based on the internal model (IM) principle. The proposed method is experimentally validated in virtual and real scenarios, where the hexapod walking robot has to recognize and adapt to leg paralysis and then recognize the recovery. The method generates novel behaviors in real time, improving the performance and outperforming the examined state-of-the-art methods. Furthermore, the robot's decisions and gained knowledge are interpretable and promise further functional scalability.