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：长期非抓取平面操纵是机器人规划和反馈控制的一项具有挑战性的任务，其特点是欠驱动、混合控制和接触不确定性。主要困难在于确定连续和离散的接触配置，例如接触点和模式，这需要联合逻辑和几何推理。为了解决这个问题，我们提出了一种演示引导的分层优化框架，实现离线任务和运动规划（TAMP）。我们的工作将推-滑系统的动力学模型的表述扩展到包括具有面切换机制的分离模式，并通过利用人类演示解决了一个预热的TAMP问题。我们展示了我们的方法能够很好地处理当前最先进的求解器中存在的局部最小值问题，并确定任务的有效解决方案。我们在模拟实验中验证了我们的结果，并在外部干扰的情况下演示了其在一个推-滑系统上应用的可行性（使用一台真实的Franka Emika机器人）。