Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion
Ge Sun, Milad Shafiee, Peizhuo Li, Guillaume Bellegarda, Auke, Ijspeert, Guillaume Sartoretti

TL;DR
This paper introduces a hierarchical control framework for legged robots inspired by animal nervous systems, utilizing separate neural networks for rhythm generation and movement modulation, improving navigation on complex terrains.
Contribution
The work presents a novel two-network hierarchical control system that mimics biological locomotion, enhancing terrain adaptability and robustness to sensorimotor delays.
Findings
Hierarchical control improves terrain navigation.
Spinal circuits generate basic rhythm; descending pathways modulate gait.
Multi-layered control shows robustness to delays.
Abstract
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control policy that models both higher (supraspinal) and spinal cord functions. In this work, we build upon our previous research by introducing two distinct neural networks: one tasked with modulating the frequency and amplitude of CPGs to generate the basic locomotor rhythm (referred to as the spinal policy, SCP), and the other responsible for receiving environmental perception data and directly modulating the rhythmic output from the SCP to execute precise movements on challenging terrains (referred to as the descending modulation policy). This division of labor more closely mimics the hierarchical…
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Taxonomy
TopicsRobotic Locomotion and Control
