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.

翻译：设计接触密集型操作的规划器与控制器极具挑战性，因为接触破坏了众多基于梯度的控制器综合工具所依赖的平滑性条件。接触平滑化通过用平滑系统近似非平滑系统，使这些综合工具得以更有效地应用。然而，将经典控制综合方法应用于平滑化接触动力学的研究仍相对不足。本文分析了基于接触平滑化的微分仿真器在综合线性控制器方面的效能。我们提出了利用接触平滑化计算以下两类基准方法的自然框架：(a) 对不确定条件和/或动力学具有鲁棒性的开环规划，以及(b) 用于稳定开环规划轨迹的反馈增益。以机器人双臂全身操作为实验平台，我们对超过300条轨迹进行了广泛的实证研究，并分析了为何LQR方法在稳定接触密集型规划方面似乎存在不足。