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
Abstract : We address the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a post- impact 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 post- impact. 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 incor- porating 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.
Type de document :
Communication dans un congrès
IEEE International Conference on Robotics and Automation, May 2017, Singapour, Singapore. Proceedings of the 2017 IEEE International Conference on Robotics and Automation
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Vincent Samy, Karim Bouyarmane, Abderrahmane Kheddar. QP-based Adaptive-Gains Compliance Control in Humanoid Falls. IEEE International Conference on Robotics and Automation, May 2017, Singapour, Singapore. Proceedings of the 2017 IEEE International Conference on Robotics and Automation. 〈hal-01365108v2〉

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