面向人形机器人多接触遥操作的稳定性感知重定向方法
Stability-Aware Retargeting for Humanoid Multi-Contact Teleoperation
AI总结：
针对人形机器人多接触遥操作中易失稳、转矩饱和的问题，提出基于质心稳定性的重定向方法，通过稳定裕度梯度动态调整接触点与姿态，仿真与硬件实验均验证其能提升稳定裕度及抗冲击、转矩裕度表现。
摘要：
遥操作是生成参考运动、使人形机器人能够执行各类任务的有效方法。但在涉及手部接触和非共面表面的场景下，遥操作颇具挑战，常导致电机转矩饱和或因滑动失去稳定性。我们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，以提升这类复杂场景下的稳定性。该方法的核心是对稳定裕度梯度的高效解析计算，利用该梯度可识别稳定性对遥操作设定值高度敏感的场景，并指导这些设定值的局部调整。我们通过在仿真和硬件平台上遥操作人形机器人完成操纵任务对该框架进行了验证，结果表明稳定裕度得到提升。我们还通过实验证明，更高的稳定裕度与更强的冲击恢复能力和关节转矩裕度呈正相关。
英文摘要：
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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