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

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.

A Comparative Study of Adaptive Interlimb Coordination Mechanisms for Self-Organized Robot Locomotion

“…This suggests that adaptive parameter values of the PM and PR are necessary in various situations. Recently, some studies have implemented learning techniques to obtain adaptive sensory feedback gains of the PM mechanisms (Sun et al, 2018;Dujany et al, 2020;Miguel-Blanco and Manoonpong, 2020).…”

“…To demonstrates these mechanisms, biologists have proposed some neurological principles, such as central pattern generators (CPGs) (Marder and Bucher, 2001), reflex chains (Grillner, 1975), and sensory feedback (Grillner, 2003;Rossignol et al, 2006), through biological experiments. In addition, roboticists have developed many bio-inspired neural control schemes for legged robots to emulate animal-like self-organized locomotion (Kimura et al, 2007;Owaki et al, 2013;Barikhan et al, 2014;Ambe et al, 2018;Fukui et al, 2019;Miguel-Blanco and Manoonpong, 2020). To realize self-organized locomotion and adaptation on artificial legged systems, many adaptive robot control schemes based on distributed abstract CPGs incorporating ground reaction force (GRF) feedback have been proposed (Kimura et al, 2007;Owaki et al, 2013;Barikhan et al, 2014;Ambe et al, 2018;Fukui et al, 2019).…”

“…PM typically uses continuous GRFs to modulate CPG phases continuously (Kimura et al, 2007;Owaki et al, 2013Barikhan et al, 2014;Fukuhara et al, 2018;Miguel-Blanco and Manoonpong, 2020). In contrast, the PR uses discrete GRFs to reset the CPG phases intermittently (Tsujita et al, 2001;Aoi and Tsuchiya, 2007;Nomura et al, 2009;Aoi et al, 2010Aoi et al, , 2012Ambe et al, 2018).…”

Insect-Inspired Robots: Bridging Biological and Artificial Systems

“…For the phase adaptation, typically ground reaction force (GRF) feedback is employed to reset or continuously modulate the phase relationships between the oscillators. Two standard mechanisms for phase adaptation, resulting in adaptive interlimb coordination for self-organized robot locomotion, are phase resetting (PR) [181] and continuous phase modulation (PM) [159,182] (Figure 6a, upper inset). PR uses discrete GRFs to intermittently reset CPG phases while PM uses continuous GRFs to modulate CPG phases.…”

“…In this approach, the trajectory of the end of a foot (consisting of stance and swing phases) is designed and the IK translates the trajectory into the robot joint angle. For the trajectory design, one simple way is to use a straight or almost straight profile for the stance phase and an arch profile and swing phase [159]. An alternative way is to record an animal leg trajectory during locomotion and use it as the desired robot leg trajectory [66,126,204].…”