发表机构
Korea Institute of Science and Technology (KIST); Korea University（韩国科学技术研究院（KIST）; 高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于MPPI的任务空间控制框架，结合扭矩采样架构与GPU并行化，实现非结构化环境中机器人操作臂的柔顺力感知控制，求解更新速率超166Hz，经7自由度操作臂实验验证有效。
AI 中文摘要
本研究提出一种新颖的基于模型预测路径积分（MPPI）的任务空间控制框架。该框架在实时模型预测控制（MPC）公式中明确求解刚体动力学并施加安全约束，实现精确的运动与力控制，使机器人操作臂在非结构化环境中进行安全有效的物理交互时产生柔顺行为。通过利用MPPI，该框架可高效处理传统MPC方法难以实时求解的非线性动力学。此外，我们开发了基于扭矩采样的控制架构，可高效利用GPU并行化，实现有效的柔顺与力感知行为。最终，该框架在0.18秒预测时域下达到超过166Hz的求解器更新速率，其性能通过7自由度操作臂的真实世界实验得到验证。
英文摘要
This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The proposed framework explicitly solves rigid-body dynamics within a real-time MPC formulation and enforces safety constraints, enabling accurate motion and force control that yields compliant behaviors for safe and effective physical interaction of robotic manipulators in unstructured environments. By leveraging MPPI, the proposed framework efficiently handles nonlinear dynamics that are difficult to solve with conventional MPC approaches in real-time. Furthermore, we develop a torque-sampling-based control architecture that enables efficient exploitation of GPU-based parallelization, resulting in effective compliant and force-aware behaviors. As a result, the proposed framework achieves a solver update rate of over 166 Hz with a 0.18 s prediction horizon, and its performance is validated through real-world experiments on a 7-DoF manipulator.
Comments8 pages, 6 figures. Accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
