Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Abstract

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review

“…Manoonpong and his colleagues developed a series of modular neural CPG-based locomotion control for legged robots (Manoonpong et al, 2008 , 2013 ; Steingrube et al, 2010 ; Goldschmidt et al, 2014 ; Xiong et al, 2014 , 2015 ; Dasgupta et al, 2015 ; Grinke et al, 2015 ). They showed that using this control approach leads to adaptive interlimb coordination that allows the robots to deal with complex environments, such as walking over difficult terrain (Steingrube et al, 2010 ; Manoonpong et al, 2013 ; Goldschmidt et al, 2014 ; Xiong et al, 2014 , 2015 ; Dasgupta et al, 2015 ) and avoiding obstacles in an unknown cluttered area (Manoonpong et al, 2008 ; Grinke et al, 2015 ), as observed in insects. For example, they implemented modular neural control with an adaptive chaotic CPG-based network and sensory feedback on a hexapod robot (Figures 4A,B ; Steingrube et al, 2010 ).…”

“…The robot efficiently walked on different surfaces including sponge, gravel, fine gravel, and grass. For adaptation to the avoidance of obstacles in a cluttered environment, an adaptive neural sensory processing network with synaptic plasticity was introduced to the modular neural control (Grinke et al, 2015 ). The adaptive processing network could drive different turning behaviors with short-term robot memory.…”

“…(D) Adaptive obstacle avoidance in a simulated complex environment with obstacles, sharp corners, and narrow passages. These figures were modified from Grinke et al ( 2015 ) and Steingrube et al ( 2010 ).…”

“…This regulation is indirectly achieved through premotor neuron networks (Buschmann et al, 2015 ). Goldschmidt et al ( 2014 ) and Grinke et al ( 2015 ) employed this strategy by using visual feedback for environment-dependent adaptation, such as hexapod robots climbing over an obstacle or turning away from it.…”

Global Positioning System-Based Stimulation for Robo-Pigeons in Open Space

“…Engineers have not developed any device with odor detection capabilities comparable to those of canines. Furthermore, some animals can traverse a variety of terrain types more efficiently than electromechanical robots or humans (Grinke et al, 2015 ); for instance, engineers have not developed a micro air vehicle with flight abilities comparable to those of pigeons. Animals could be employed to conduct search and rescue missions more efficiently than electromechanical robots if they could be controlled.…”