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QP-based Adaptive-Gains Compliance Control in Humanoid Falls

Vincent Samy 1 Karim Bouyarmane 2 Abderrahmane Kheddar 3
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
3 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Résumé : We address the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a postimpact stage. In the pre-impact stage, geometrical reasoning allows the robot to choose appropriate impact points in the surrounding environment and to adopt a posture to reach them while avoiding impact-singularities and preparing for the postimpact. The surrounding environment can be unstructured and may contain cluttered obstacles. The post-impact stage uses a quadratic program controller that adapts on-line the joint proportional-derivative (PD) gains to make the robot compliant-to absorb impact and post-impact dynamics, which lowers possible damage risks. This is done by a new approach incorporating the stiffness and damping gains directly as decision variables in the QP along with the usually-considered variables of joint accelerations and contact forces. Constraints of the QP prevent the motors from reaching their torque limits during the fall. Several experiments on the humanoid robot HRP-4 in a full-dynamics simulator are presented and discussed.
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Submitted on : Wednesday, March 22, 2017 - 10:40:58 AM
Last modification on : Tuesday, June 18, 2019 - 12:06:37 PM
Document(s) archivé(s) le : Friday, June 23, 2017 - 12:16:32 PM


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Vincent Samy, Karim Bouyarmane, Abderrahmane Kheddar. QP-based Adaptive-Gains Compliance Control in Humanoid Falls. ICRA: International Conference on Robotics and Automation, May 2017, Singapour, Singapore. pp.4762-4767, ⟨10.1109/ICRA.2017.7989553⟩. ⟨hal-01365108v2⟩



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