Torque Controlled Locomotion of a Biped Robot with Link Flexibility
When a big and heavy robot moves, it exerts large forces on the environment and on its own structure, its angular momentum can varysubstantially, and even the robot's structure can deform if there is a mechanical weakness. Under these conditions, standard locomotion controllers can fail easily. In this article, we propose a complete control scheme to work with heavy robots in torque control. The full centroidal dynamics is used to generate walking gaits online, link deflections are taken into account to estimate the robot posture and all postural instructions are designed to avoid conflicting with each other, improving balance. These choices reduce model and control errors, allowing our centroidal stabilizer to compensate for the remaining residual errors. The stabilizer and motion generator are designed together to ensure feasibility under the assumption of bounded errors. We deploy this scheme to control the locomotion of the humanoid robot Talos, whose hip links flex when walking. It allows us to reach steps of 35~cm, for an average speed of 25~cm/sec, which is among the best performances so far for torque-controlled electric robots.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single locomotion skill, we develop a general co…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Learning Bipedal Walking for Humanoids with Current Feedback
Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such a…
Deep Reinforcement LearningReinforcement Learning (RL)Analysis of Harpy's Constrained Trotting and Jumping Maneuver
This study presents an analysis of experimental data from Harpy, a thruster-assisted bipedal robot developed at Northeastern University. The study examines data sets from trotting and jumping experiments to understand th…
Human Imitated Bipedal Locomotion with Frequency Based Gait Generator Network
Learning human-like, robust bipedal walking remains difficult due to hybrid dynamics and terrain variability. We propose a lightweight framework that combines a gait generator network learned from human motion with Proxi…
Reinforcement LearningHeight Control and Optimal Torque Planning for Jumping With Wheeled-Bipedal Robots
This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinear estimation, and…