Real-Time Dynamics-Based Torque-Sampling MPPI for Compliant and Force Aware Manipulation

2026-09-01

AI Summary

研究提出了一种基于MPPI的任务空间控制框架，通过实时求解刚体动力学并利用扭矩采样控制架构实现高效并行计算，从而在非结构化环境中实现安全有效的机器人操作。

Abstract

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.

Problem

• Real-Time
• Compliant Manipulation
• Force Control
• Unstructured Environments

Innovation

• Model Predictive Path Integral (MPPI)
• real-time MPC
• torque-sampling
• GPU-based parallelization
• compliant and force-aware manipulation