Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push through other objects to get to the desired grasp pose, instead of deliberate prehensile rearrangement of the scene. For each object in a scene, depending on its properties, the robot may or may not be allowed to make contact with, tilt, or topple it. To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physics-based simulator to evaluate the complex multi-body interactions caused by robot actions. Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming. In this work, we show that (i) manipulation tasks (specifically pick-and-place style tasks from a tabletop or a refrigerator) can often be solved by restricting robot-object interactions to adaptive motion primitives in a plan, (ii) these actions can be incorporated as subgoals within a multi-heuristic search framework, and (iii) limiting interactions to these actions can help reduce the time spent querying the simulator during planning by up to 40x in comparison to baseline algorithms. Our algorithm is evaluated in simulation and in the real-world on a PR2 robot using PyBullet as our physics-based simulator. Supplementary video: \url{https://youtu.be/ABQc7JbeJPM}.

翻译：杂乱场景中的机器人操作通常需要与物体进行接触交互。相较于有目的地进行预抓取式场景重排，采用非抓取式动作（例如通过推动其他物体到达目标抓取位姿）可能更具经济性。场景中每个物体根据其物理属性，机器人可能被允许或禁止接触、倾斜或推倒该物体。为确保非抓取式交互满足这些约束，规划器可调用基于物理的仿真器评估机器人动作引发的复杂多体交互。然而，由于每次仿真耗时较长，在典型规划问题中需要对数千个动作进行仿真评估是不可行的。本研究证明：(i) 操作任务（特别是台面或冰箱内的抓取-放置类任务）通常可通过将机器人-物体交互限制为规划中的自适应运动基元来求解；(ii) 这些动作可作为子目标融入多启发式搜索框架；(iii) 与基线算法相比，将交互限制为这些动作可将规划期间仿真器调用时间减少高达40倍。我们在PR2机器人上使用PyBullet作为物理仿真器，在仿真环境和真实场景中进行了算法评估。补充视频：\url{https://youtu.be/ABQc7JbeJPM}。