用于实时双手机器人遥操作的类人冗余度解析方法
Human-Like Redundancy Resolution for Real-Time Bimanual Humanoid Teleoperation
AI总结：
针对实时双手机器人遥操作中仅跟踪腕部导致的肘部漂移等问题，提出结合自适应肘部引导与奇异鲁棒腕部跟踪的类人冗余度解析框架，实验验证其精度与效率提升，适用于多种机器人形态。
摘要：
实时双手机器人遥操作需要精准的腕部跟踪，同时保持手臂构型可预测。由于7自由度（DoF）手臂在相同腕部位姿下允许多种肘部构型，仅跟踪腕部会导致肘部漂移或构型突变。固定拟人约束可稳定肘部姿态，但也会阻碍奇异构型附近所需的冗余调整。我们提出一种类人冗余度解析框架，结合自适应肘部引导与奇异鲁棒腕部跟踪。肩-腕几何重定向生成机器人尺度的腕部目标。自适应肘部偏移准则利用偏移方向引导姿态，并按偏移幅度缩放该引导，使人臂伸直时冗余自由度逐步释放。分层速度求解器优先处理腕部跟踪，并将自适应肘部引导投影到腕部任务的零空间中。在固定腕部目标的 ablation 实验中，与移除该引导相比，自适应引导使平均肘部方向误差降低54.4%。在工作空间边界压力测试中，与无阻尼伪逆相比，奇异鲁棒跟踪使峰值原始关节速度需求降低97.9%。真实世界双手机器人任务及跨平台重定向实验表明，该方法适用于多种机器人形态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
