Long-term non-prehensile planar manipulation is a challenging task for robot planning and feedback control. It is characterized by underactuation, hybrid control, and contact uncertainty. One main difficulty is to determine both the continuous and discrete contact configurations, e.g., contact points and modes, which requires joint logical and geometrical reasoning. To tackle this issue, we propose a demonstration-guided hierarchical optimization framework to achieve offline task and motion planning (TAMP). Our work extends the formulation of the dynamics model of the pusher-slider system to include separation mode with face switching mechanism, and solves a warm-started TAMP problem by exploiting human demonstrations. We show that our approach can cope well with the local minima problems currently present in the state-of-the-art solvers and determine a valid solution to the task. We validate our results in simulation and demonstrate its applicability on a pusher-slider system with a real Franka Emika robot in the presence of external disturbances.

翻译：长时间非抓取式平面操作是机器人规划与反馈控制中的一个具有挑战性的任务，其特征包括欠驱动、混合控制以及接触不确定性。其中一个主要难点在于确定连续和离散的接触配置，例如接触点与接触模式，这需要联合的逻辑与几何推理。为解决这一问题，我们提出了一种演示引导的分层优化框架，以实现离线任务与运动规划（TAMP）。我们的工作扩展了推杆-滑块系统动力学模型的表述，使其包含带有面切换机制的分离模式，并通过利用人类演示来解决暖启动的TAMP问题。我们表明，该方法能够有效应对当前最先进求解器中存在的局部最小值问题，并为任务确定一个有效的解决方案。我们在仿真中验证了结果，并在存在外部干扰的实际Franka Emika机器人推杆-滑块系统中展示了其适用性。