Reinforcement Learning-Based Parameter Optimization for Whole-Body Admittance Control with IS-MPC

Nicolas Figueroa, Julio Tafur, Abderrahmane Kheddar

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Resumen

Maintaining stability in bipedal walking remains a significant challenge in humanoid robotics, largely due to the numerous involved hyperparameters. Traditional methods for determining these hyperparameters, such as heuristic approaches, can be both time-consuming and potentially suboptimal. In this paper, we present an approach aimed at enhancing the stability of bipedal gait, particularly when faced with floor perturbations and speed variations. Our main contribution is the integration of intrinsically stable model predictive control (IS-MPC) and whole-body admittance control within a closed-loop reinforcement learning system. We devised a reinforcement learning plugin, implemented in the mc-rtc framework, that allows the control system to continuously monitor the robot's current states, maintain recursive feasibility, and optimize parameters in real-time. Furthermore, we propose a reward function derived from a combination of changes in single and double support time, postural recovery, divergent control of motion, and action generation grounded in training optimization. In the course of this research, we conducted experiments on a real humanoid robot to validate initial aspects of our work. The integrated module's effectiveness was further assessed through comprehensive simulations.

Idioma originalInglés
Título de la publicación alojada2024 IEEE/SICE International Symposium on System Integration, SII 2024
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1405-1410
Número de páginas6
ISBN (versión digital)9798350312072
DOI
EstadoPublicada - 2024
Evento2024 IEEE/SICE International Symposium on System Integration, SII 2024 - Ha Long, Vietnam
Duración: 8 ene. 202411 ene. 2024

Serie de la publicación

Nombre2024 IEEE/SICE International Symposium on System Integration, SII 2024

Conferencia

Conferencia2024 IEEE/SICE International Symposium on System Integration, SII 2024
País/TerritorioVietnam
CiudadHa Long
Período8/01/2411/01/24

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