A Bio-inspired Modular System for Humanoid Posture Control

TL;DR

This paper introduces a bio-inspired, modular posture control system for humanoid robots based on neuroscience principles, enabling scalable, robust, and human-like compliance through disturbance estimation and multisensory feedback.

Contribution

It presents a generalized DEC control module for multi-DOF robots, integrating multisensory feedback for improved robustness and scalability in humanoid posture control.

Findings

Successful proof-of-principle tests in 1 and 2 DOF robots

Modular control architecture scales linearly with DOFs

Enhanced error robustness compared to monolithic control

Abstract

Bio-inspired sensorimotor control systems may be appealing to roboticists who try to solve problems of multiDOF humanoids and human-robot interactions. This paper presents a simple posture control concept from neuroscience, called disturbance estimation and compensation, DEC concept [1]. It provides human-like mechanical compliance due to low loop gain, tolerance of time delays, and automatic adjustment to changes in external disturbance scenarios. Its outstanding feature is that it uses feedback of multisensory disturbance estimates rather than 'raw' sensory signals for disturbance compensation. After proof-of-principle tests in 1 and 2 DOF posture control robots, we present here a generalized DEC control module for multi-DOF robots. In the control layout, one DEC module controls one DOF (modular control architecture). Modules of neighboring joints are synergistically interconnected using vestibular information in combination with joint angle and torque signals. These sensory interconnections allow each module to control the kinematics of the more distal links as if they were a single link. This modular design makes the complexity of the robot control scale linearly with the DOFs and error robustness high compared to monolithic control architectures. The presented concept uses Matlab/Simulink (The MathWorks, Natick, USA) for both, model simulation and robot control and will be available as open library