外观
论文arXiv 2610.2123913 天前
CLIS-MPPI：执行延迟下具有初始状态采样的闭环模型预测路径积分控制
针对执行延迟导致的状态不匹配，提出CLIS-MPPI，并在UR5e真实窄通道任务中验证。
Universal Robots UR5e原文链接：CLIS-MPPI: Closed-Loop Model Predictive Path Integral Control With Initial-State Sampling Under Execution Delay（arXiv 2610.21239 · 发布日期：2026-10-15）
CLIS-MPPI: Closed-Loop Model Predictive Path Integral Control With Initial-State Sampling Under Execution Delay
针对执行延迟导致的状态不匹配，提出CLIS-MPPI，并在UR5e真实窄通道任务中验证。
核心要点
- 真实机器人中规划延迟会造成仿真初态与执行时实际状态不匹配，降低精度并可能碰撞。
- 方法将延迟建模为执行初态分布，采样初态做rollout优化，并以rollout局部反馈校正。
- 在UR5e窄通道避障与末端到达任务中，标准MPPI 5次中2次碰撞，CLIS-MPPI 5次均无碰撞。
- 无碰撞运行平均看，CLIS-MPPI将完成时间25.4s降至24.4s，路径0.456m降至0.425m，并降低关节RMSE。
验证范围与局限：验证范围为6-DoF UR5e机械臂真实实验，在窄通道避障与末端目标到达任务上进行5次运行。
英文摘要 (Abstract)
Sampling-based motion planning such as Model Predictive Path Integral (MPPI) control has become attractive for robotics due to its ability to handle nonlinear dynamics and non-smooth objectives without gradient information, while leveraging parallel rollout computation for real-time performance. However, in real-world robotic systems, the optimized control input is often applied after a non-negligible planning delay, causing a mismatch between the state used in forward simulation and the actual state at control execution, which can significantly degrade planning accuracy and may even lead to unsafe behavior such as collisions. This paper proposes Closed-Loop MPPI with Initial-State Sampling (CLIS-MPPI), which explicitly addresses execution-time state mismatch by modeling planning delay as a distribution over plausible execution-time initial states, performing rollout optimization from sampled initial states, and applying a rollout-based local feedback correction during control execution. The proposed method is validated through real-world experiments on a 6-DoF UR5e manipulator in a narrow-passage task involving obstacle avoidance and end-effector target reaching. Across 5 real-world runs of the same task, standard MPPI collided in 2 runs, whereas CLIS-MPPI remained collision-free in all 5 runs. Averaged over all collision-free runs for each method (3 for MPPI and 5 for CLIS-MPPI), CLIS-MPPI reduced the average task completion time from 25.4s to 24.4s and the average end-effector path length from 0.456 m to 0.425 m. It also reduced the overall joint-position and joint-velocity root mean square errors (RMSEs) from 0.100 rad and 0.108 rad/s to 0.017 rad and 0.011 rad/s, respectively. These results demonstrate improved safety, planning-execution consistency, and motion smoothness.
作者：Zidong Liu, Xu Chen
来源
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