General Distributed Neural Control and Sensory Adaptation for Self-Organized Locomotion and Fast Adaptation to Damage of Walking Robots.

Aitor Miguel-Blanco,Poramate Manoonpong

doi:10.3389/fncir.2020.00046

Abstract

Walking animals such as invertebrates can effectively perform self-organized and robust locomotion. They can also quickly adapt their gait to deal with injury or damage. Such a complex achievement is mainly performed via coordination between the legs, commonly known as interlimb coordination. Several components underlying the interlimb coordination process (like distributed neural control circuits, local sensory feedback, and body-environment interactions during movement) have been recently identified and applied to the control systems of walking robots. However, while the sensory pathways of biological systems are plastic and can be continuously readjusted (referred to as sensory adaptation), those implemented on robots are typically static. They first need to be manually adjusted or optimized offline to obtain stable locomotion. In this study, we introduce a fast learning mechanism for online sensory adaptation. It can continuously adjust the strength of sensory pathways, thereby introducing flexible plasticity into the connections between sensory feedback and neural control circuits. We combine the sensory adaptation mechanism with distributed neural control circuits to acquire the adaptive and robust interlimb coordination of walking robots. This novel approach is also general and flexible. It can automatically adapt to different walking robots and allow them to perform stable self-organized locomotion as well as quickly deal with damage within a few walking steps. The adaptation of plasticity after damage or injury is considered here as lesion-induced plasticity. We validated our adaptive interlimb coordination approach with continuous online sensory adaptation on simulated 4-, 6-, 8-, and 20-legged robots. This study not only proposes an adaptive neural control system for artificial walking systems but also offers a possibility of invertebrate nervous systems with flexible plasticity for locomotion and adaptation to injury.

Highlights

Walking animals show robust and adaptive locomotion
While animal experiments show that synaptic connections in sensory motor pathways are plastic (Whelan and Pearson, 1997; Wolf and Büschges, 1997; Wark et al, 2007) to allow for stable locomotion and adaptation, this plasticity with continuous synaptic changes has been largely ignored in robotic implementation
In this study, we introduce a fast learning mechanism for continuous online adaptation or flexible plasticity in sensory pathways in order to (i) generate stable self-organized locomotion, (ii) deal with damage, and (iii) be able to automatically adapt to different walking robots

Summary

Introduction

Walking animals show robust and adaptive locomotion. They can form their gaits in a self-organized manner as well as quickly adapt to environmental and body changes, including damage (Wolf and Büschges, 1997; Büschges and Manira, 1998; Grabowska et al, 2012). Biological investigation reveals that the adaptive coordination in walking animals is largely attained by distributed neural control mechanisms with central pattern generators (CPGs), local or proprioceptive feedback, and body dynamics (Pearson and Iles, 1970, 1973; Bässler and Wegner, 1983; Dean, 1989; Berkowitz and Laurent, 1996). Machine learning techniques are employed first to optimize the connections through simulation before implementing them on real robots (Hwangbo et al, 2019) Unexpected situations such as leg damage might lead to unstable locomotion if the sensory connection strength cannot be automatically or continuously adjusted to deliver proper information for adaptation. Transferring the control system with the tuned or optimized connections from one walking robot to another might not work effectively

Methods

Results

Conclusion