Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

Abstract

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

“…55,56 Finally, we argue that the performance could be improved by using advanced techniques for optimization of control parameters. 57,58…”

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

“…There are also six-legged robots called hexapods. In [41], various control architectures are applied in the four-legged robot to monitor robot behavior. These procedures are base on bio-inspired structures help in carrying heavy loads for their dynamic stability and heavy equipment carrying capacity through the rough surface [41].…”

Online Learning of the Dynamical Internal Model of Transfemoral Prosthesis for Enhancing Compliance

“…For each joint i ∈ {a; k}, the desired torque is expressed as τ i des = τ i FB +τ i pred , where the feedback torque component τ i FB is computed by a proportional-derivative (PD) controller and the predictive torque component τ i pred relies on iterative learning of a dynamical internal model. This control architecture -and in particular its prediction module -is inspired from [26], [27].…”

“…This dynamical model is incrementally learned by LWPR, an algorithm that has been successfully used in different simulation studies, e.g. [26], [27]. In our approach, each prosthesis joint i ∈ {a; k} has its own LWPR module, computing τ i pred from the set of sensory inputs x.…”