aiqbench analysis
This research advances real-time robotic control by integrating Model Predictive Path Integral (MPPI) with torque-sampling architectures to achieve high-frequency compliant manipulation on unstructured environments. By leveraging GPU parallelization, the framework solves nonlinear rigid-body dynamics at over 166 Hz, enabling safe physical interaction that conventional MPC methods struggle to handle efficiently. The work represents a significant step forward in making advanced force-aware robotics practical for dynamic tasks.
Real-Time Dynamics-Based Torque-Sampling MPPI for Compliant and Force Aware Manipulation
arXiv:2609.02020v1 Announce Type: new 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.
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.
8 pages, 6 figures. Accepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Robotics (cs.RO)
arXiv:2609.02020 [cs.RO]
(or arXiv:2609.02020v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.02020 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 236 words
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