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Conference Papers Year : 2021

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Abstract

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 msecs. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.
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Dates and versions

hal-02995796 , version 1 (09-11-2020)
hal-02995796 , version 2 (17-05-2021)

Identifiers

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Ewen Louis Dantec, Rohan Budhiraja, Adria Roig, Teguh Lembono, Guilhem Saurel, et al.. Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos. International Conference on Robotics and Automation (ICRA 2021), IEEE, May 2021, Xi'an, China. ⟨10.1109/ICRA48506.2021.9560742⟩. ⟨hal-02995796v2⟩
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