Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion
arxiv(2024)
摘要
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 locomotor
control systems observed in legged animals, thereby enhancing the robot's
ability to navigate various uneven surfaces, including steps, high obstacles,
and terrains with gaps. Additionally, we investigate the impact of sensorimotor
delays within our framework, validating several biological assumptions about
animal locomotion systems. Specifically, we demonstrate that spinal circuits
play a crucial role in generating the basic locomotor rhythm, while descending
pathways are essential for enabling appropriate gait modifications to
accommodate uneven terrain. Notably, our findings also reveal that the
multi-layered control inherent in animals exhibits remarkable robustness
against time delays. Through these investigations, this paper contributes to a
deeper understanding of the fundamental principles of interplay between spinal
and supraspinal mechanisms in biological locomotion. It also supports the
development of locomotion controllers in parallel to biological structures
which are ...
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