Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

Abstract

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

“…The presence of an actuated joint in the robot body was exploited to improve the capabilities of the system to face with complex situations including gaps and obstacles (Goldschmidt et al, 2014; Dasgupta et al, 2015). In Pavone et al (2006), the sprawled posture was a key element for solving the obstacle-climbing issue.…”

Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

“…AMOS and HECTOR are two robots which are built around machine learning of specific tasks. AMOS is controlled by a large recurrent neural network trained by reservoir computing methods to estimate the leg's state and anticipate future sensory information (Dasgupta et al, 2015). HECTOR uses many feedforward artificial neural networks to map between different states, such as mapping joint angles to the height of a leg (Schilling et al, 2013a).…”

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

“…Unsupervised tuning methods, in contrast, tune the model based on how well the model accomplishes a task, such as navigating toward a goal, without comparison to animal data. These methods frequently use genetic algorithms (GAs) (Beer and Gallagher, 1992 ; Haferlach et al, 2007 ; Agmon and Beer, 2013 ; Izquierdo and Beer, 2013 ) or reservoir computing (RC) (Dasgupta et al, 2015 ) to test many different networks and parameter values, based on a simulated agent’s performance. GAs can be effective at finding networks that perform specific operations, such as oscillating (Beer and Gallagher, 1992 ), navigating (Haferlach et al, 2007 ), or switching between foraging tasks (Agmon and Beer, 2013 ).…”