发表机构
School of Mechanical Engineering, Kyung Hee University; Institutes of Convergence Technology（庆熙大学机械工程学院; 融合技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对MPPI处理操纵约束的缺陷，提出PR-MPPI算法，通过投影与回退机制精确强制执行约束，在仿真与真实硬件的双臂系统上验证了其有效性。
AI 中文摘要
模型预测路径积分（MPPI）控制因无梯度、可并行处理非凸代价，被广泛应用于操纵任务。但操纵任务常要求运动全程满足约束：如双臂抓取形成的闭合运动链需保持精确，或关节极限、避障要求全程不被突破。MPPI仅通过将约束作为软惩罚融入代价处理，约束仅近似满足，在任务代价较强时会失效。为解决该问题，本文提出投影-回退MPPI（PR-MPPI），在采样动力学内部强制执行约束。每次滚动步中，采样速度会被投影以满足两类约束：等式约束将其限制在子空间内，每个不等式约束则限制在该子空间的半空间内，因此不等式处理不会破坏等式约束。不过，该投影仅能满足一阶约束，有限步长会导致偏离等式的微小漂移，因此本文将返回的指令回退至数值容差范围内，且与任务权重无关。本文在14自由度双臂系统上验证PR-MPPI：仿真中，返回指令在关节极限压力测试和随机避障场景下，均使闭合链等式满足数值容差；真实硬件测试中，Unitree H1-2人形机器人的机械臂可主动躲避移动障碍物。代码与实验视频可在指定URL获取。
英文摘要
Model Predictive Path Integral (MPPI) control is widely used in manipulation for its gradient-free, parallel handling of non-convex costs. Manipulation tasks, however, often impose constraints that hold throughout the motion: a closed kinematic chain that two grasping arms keep exactly, or joint limits and obstacle clearances that are never crossed. MPPI handles such constraints only through the cost, as soft penalties that hold approximately and fail under a strong task cost. To address this, we propose Projection-Retraction MPPI (PR-MPPI), which enforces the constraints inside the sampled dynamics. At every rollout step, the sampled velocity is projected to satisfy both constraint types: the equality restricts it to a subspace, and each inequality to a half-space within that subspace, so inequality handling never breaks the equality. This projection, however, satisfies the constraints only to first order, and a finite step leaves a small drift off the equality. Therefore, we retract the returned command back onto the constraint to numerical tolerance and independent of task weighting. We validate PR-MPPI on 14-DoF dual-arm systems. In simulation, the returned commands satisfy the closed-chain equality to numerical tolerance through a joint-limit stress test and randomized obstacle avoidance. On real hardware, the arms of a Unitree H1-2 humanoid reactively avoid a moving obstacle. Code and experiment videos are available at https://rcilab.github.io/prmppi.
