通过动态调整接触点与姿态提升人形机器人在复杂接触下的操作稳定性
Stability-Aware Retargeting for Humanoid Multi-Contact Teleoperation
- 基于质心稳定性分析，实时计算稳定性梯度并优化操作设定
- 仿真与硬件实验显示稳定性裕度显著提升，抗冲击能力增强
- 适合需要高稳定性的复杂场景人形机器人远程操控应用
遥操作是生成参考运动、使类人机器人执行多样化任务的有效方法。然而，在使用手部接触和非共面表面时，遥操作面临挑战，常导致电机扭矩饱和或因滑动而失稳。本文提出一种基于质心稳定性的重定向方法，可在遥操作过程中动态调整接触点与姿态，以增强复杂场景下的稳定性。核心在于高效解析计算稳定性裕度梯度，识别对遥操作设定敏感的不稳定情形，并据此局部调整设定。我们在仿真与硬件上验证了该框架在类人机器人上执行操纵任务的效果，结果表明稳定性裕度显著提高。实证还显示，更高的稳定性裕度与更强的抗冲击能力及关节扭矩余量正相关。
原文摘要 · Abstract (English)
Teleoperation is a powerful method to generate reference motions and enable humanoid robots to perform a broad range of tasks. However, teleoperation becomes challenging when using hand contacts and non-coplanar surfaces, often leading to motor torque saturation or loss of stability through slipping. We propose a centroidal stability-based retargeting method that dynamically adjusts contact points and posture during teleoperation to enhance stability in these difficult scenarios. Central to our approach is an efficient analytical calculation of the stability margin gradient. This gradient is used to identify scenarios for which stability is highly sensitive to teleoperation setpoints and inform the local adjustment of these setpoints. We validate the framework in simulation and hardware by teleoperating manipulation tasks on a humanoid, demonstrating increased stability margins. We also demonstrate empirically that higher stability margins correlate with improved impulse resilience and joint torque margin.
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