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：长期非抓取平面操控是机器人规划与反馈控制中的一项挑战性任务，其特点包括欠驱动、混合控制及接触不确定性。核心难点在于同时确定连续与离散的接触构型（例如接触点与接触模式），这需要联合逻辑与几何推理。为解决这一问题，我们提出了一种基于演示的分层优化框架，以实现离线任务与运动规划（TAMP）。本研究扩展了推-滑系统动力学模型的公式化，纳入了包含面切换机制的分离模式，并通过利用人类演示求解了具有热启动的TAMP问题。实验表明，我们的方法能有效应对当前最先进求解器中的局部极小值问题，并为任务确定有效解。通过仿真验证了结果，并在存在外部干扰的真实Franka Emika机器人推-滑系统上展示了其适用性。