用平滑接触动力学+线性反馈，仍难稳定复杂抓握动作。
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
- 基于接触平滑的微分模拟器设计开环轨迹与反馈增益
- 300多条轨迹实测表明LQR对接触丰富的任务稳定性不足
- 为双臂全身操作提供可复现的基准测试方法
为实现接触丰富的操作规划与控制，需应对接触导致系统非光滑的挑战。接触平滑通过近似将非光滑系统转化为光滑系统，使基于梯度的控制器设计工具更易应用。本文分析了基于接触平滑的微分模拟器进行线性控制器合成的有效性。我们提出了自然基线方法，用于利用接触平滑计算：(a) 对不确定条件或动态具有鲁棒性的开环轨迹；(b) 围绕开环轨迹的反馈增益。以双臂全身操纵为测试平台，我们在超过300条轨迹上进行了大量实证实验，并分析了为何LQR在稳定接触丰富计划时表现不佳。相关视频及硬件实验演示见：https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9。
原文摘要 · Abstract (English)
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9.
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