International Journal of Robotics Research
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Neural networks for pattern recognition
Neural networks for pattern recognition
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Sensorimotor Control of Biped Locomotion
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Combining Simulation and Reality in Evolutionary Robotics
Journal of Intelligent and Robotic Systems
Ankle Actuation for Limit Cycle Walkers
International Journal of Robotics Research
Controlling the Walking Speed in Limit Cycle Walking
International Journal of Robotics Research
International Journal of Robotics Research
Fully interconnected, linear control for limit cycle walking
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Swing-Leg Retraction for Limit Cycle Walkers Improves Disturbance Rejection
IEEE Transactions on Robotics
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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The kinematics of human walking are largely driven by passive dynamics, but adaptation to varying terrain conditions and responses to perturbations require some form of active control. The basis for this control is often thought to take the form of entrainment between a neural oscillator (i.e., a central pattern generator and/or distributed counterparts) and the mechanical system. Here we use techniques in evolutionary robotics to explore the potential of a purely reactive, linear controller to control bipedal locomotion over rough terrain. In these simulation studies, joint torques are computed as weighted linear sums of sensor states, and the weights are optimized using an evolutionary algorithm. We show that linear reactive control can enable a seven-link 2D biped and a nine-link 3D biped to walk over rough terrain (steps of 5% leg length or more in the 2D case). In other words, the simulated walker gradually learns the appropriate weights to achieve stable locomotion. The results indicate that oscillatory neural structures are not necessarily a requirement for robust bipedal walking. The study of purely reactive control through linear feedback may help to reveal some basic control principles of stable walking.