Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learning (RL). However, these methods often have high sample-complexity, are susceptible to domain changes, and produce unsafe motions that a robot should not perform. On the other hand, purely geometric model-based planning can produce complex behaviors that satisfy all the geometric constraints of the robot but might not be dynamically feasible for a given environment. In this work, we leverage a geometric model-based planner to build a mixture of path-policies on which a task-specific meta-policy can be learned to complete the task. In our results, we demonstrate that a successful meta-policy can be learned to push a door, while requiring little data and being robust to model uncertainty of the environment. We tested our method on a 7-DOF Franka-Emika Robot pushing a cabinet door in simulation.

翻译：近期研究表明，诸如推拉或倾倒等复杂操作技能，可通过强化学习等先进学习技术习得。然而，这些方法通常样本复杂度高、易受领域变化影响，且可能产生机器人不应执行的不安全动作。另一方面，纯几何模型驱动的规划能生成满足所有机器人几何约束的复杂行为，但在特定环境下可能缺乏动力学可行性。本研究利用几何模型规划器构建路径策略混合体，在此基础上可学习任务特定元策略以完成任务。实验结果表明，该方法能成功学习推开柜门的元策略，所需数据量小，且对环境模型不确定性具有鲁棒性。我们在仿真环境中使用7自由度Franka-Emika机器人推开柜门的任务中对该方法进行了验证。