Human-like arm control improves real-time humanoid robot teleoperation
Human-Like Redundancy Resolution for Real-Time Bimanual Humanoid Teleoperation
Robotics
Summary
Controlling robot arms that have many joints can be tricky because the same hand position can be reached with different elbow positions, which can cause unpredictable movements. The authors developed a way to make robot arms move their elbows more like humans do, keeping movements smooth and easy to predict. Their method adjusts the elbow position as the arm straightens and avoids sudden jumps near difficult poses. Tests showed their approach greatly reduced errors and sudden joint movements while performing tasks with two robot arms.
What this means in practice
- •For robot teleoperation engineers: Improve elbow movement predictability during real-time humanoid robot control for safer and more intuitive remote bimanual tasks.
- •For industrial robot integrators: Enable better robot arm posture management near difficult positions to reduce joint stress and maintain continuous operation across different robot types.
Authors
Chao Tan, Ming Shin Cindy Sung, Shuxian Liang, Chaoqun Wan, Zhen Huang, Xian-Sheng Hua
Abstract
Real-time bimanual humanoid teleoperation requires accurate wrist tracking while keeping arm configurations predictable. Because a 7-DoF arm admits multiple elbow configurations for the same wrist pose, wrist-only tracking can cause elbow drift or abrupt configuration changes. Fixed anthropomorphic constraints can stabilize elbow posture, but they can also block the redundant adjustments needed near singular configurations. We propose a human-like redundancy-resolution framework that combines adaptive elbow guidance with singularity-robust wrist tracking. Shoulder-wrist geometric retargeting generates robot-scale wrist targets. An adaptive elbow-offset criterion uses the offset direction to guide posture and scales this guidance by the offset magnitude, so the redundant degree of freedom is progressively released as the human arm straightens. A hierarchical velocity solver prioritizes wrist tracking and projects the adaptive elbow guidance into the wrist-task null space. In a fixed-wrist-target ablation, adaptive guidance reduces mean elbow-direction error by 54.4% compared with removing this guidance. In a workspace-boundary stress test, singularity-robust tracking reduces peak raw joint-velocity demand by 97.9% compared with the undamped pseudoinverse. Real-world bimanual tasks and cross-platform retargeting show applicability across robot morphologies.