Distributed-force-feedback-based reflex with online learning for adaptive quadruped motor control

Abstract

Biological motor control mechanisms (e.g., central pattern generators (CPGs), sensory feedback, reflexes, and motor learning) play a crucial role in the adaptive locomotion of animals. However, the interaction and integration of these mechanisms – necessary for generating the efficient, adaptive locomotion responses of legged robots to diverse terrains – have not yet been fully realized. One issue is that of achieving adaptive motor control for fast postural adaptation across various terrains. To address this issue, this study proposes a novel distributed-force-feedback-based reflex with online learning (DFRL). It integrates force-sensory feedback, reflexes, and learning to cooperate with CPGs in producing adaptive motor commands. The DFRL is based on a simple neural network that uses plastic synapses modulated online by a fast dual integral learner. Experimental results on different quadruped robots show that the DFRL can (1) automatically and rapidly adapt the CPG patterns (motor commands) of the robots, enabling them to realize appropriate body postures during locomotion and (2) enable the robots to effectively accommodate themselves to various slope terrains, including steep ones. Consequently, the DFRL-controlled robots can achieve efficient adaptive locomotion, to tackle complex terrains with diverse slopes.