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Reverse-Engineering Nature for Locomotion Control in Robots and Humans

The goal of this research is to discover the physical reasons and control principles that govern movements of humans and animals and to utilize these findings to build next generation robotic devices. We have focused on the control aspect of this problem by

(1) developing an impedance controller that is capable of generating autonomous self-organized locomotion

(2) extracting controllers form experimental data consistent with human locomotion

(3) developing physics base simulation tools to predict the motion of robot locomotion

(4) developing optimization based constrained impedance controllers that simultaneously provide cyclic limb coordination and upper body stabilization during the inherently unstable task of bipedal locomotion.

Numerical simulations and experiments on an anthropomorphic biped robot demonstrate some of these ideas.

E.S. Altinkaynak and D.J. Braun, A Phase-Invariant Linear Torque-Angle-Velocity Relation Hidden in the Human Walking Data, IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2019.

L. Li and D.J. Braun, Constrained Feedback Control by Prioritized Multi-objective Optimization, IEEE International Conference on Robotics and Automation, 2019.

D.J. Braun, M. Goldfarb, A Control Approach for Actuated Dynamics Walking in Biped Robots, IEEE Transactions on Robotics, 2009.