用物体轨迹引导机器人规划，提升非抓取操作的长期成功率
Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation
- 分层设计：先规划物体路径，再以之为参考优化机器人动作
- 仿真中任务成功率提升40%，控制频率快26%；实测成功率达20%提升
- 适合需要长远推理的非抓取任务，如推动物体避障
非抓取式机器人操作的长时程规划因系统欠驱动和交互不连续而困难。本文提出一种分层模型预测路径积分（MPPI）控制方法，通过独立计算的物体级轨迹引导机器人级规划，实现高效长时程预测。首先求解一个简化物体问题（假设物体可直接控制），获得物体轨迹作为参考，用于联合求解机器人-物体规划问题。在模拟与真实实验中均使用6自由度xArm6机械臂完成推物任务，目标是使物体到达指定位置并避开静态障碍物，需具备非短期推理能力。结果表明，所提方法在仿真中任务成功率提升40%，控制频率加快26%；真实实验中成功率提升20%，计算开销与传统MPPI相当。
原文摘要 · Abstract (English)
Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchical formulation of model predictive path integral (MPPI) control that guides robot-level planning with a separately computed object-level plan to achieve efficient long-horizon prediction. We first solve a simplified object-only problem, assuming the object can be actuated directly, and use the planned object trajectory as a reference in solving the joint robot-object planning problem. We evaluate our method in both simulation and hardware using a 6-DoF xArm6 manipulator to perform object pushing tasks in which the target object must reach a goal while avoiding static obstacles, necessitating non-myopic reasoning. Our object-informed MPPI increases task success by 40\% with a 26\% faster control frequency in simulation, and by 20\% in real experiments with similar computation as regular MPPI.
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