Problem
Research questions and friction points this paper is trying to address.
Robot Motion Planning
Complex Environment
Multi-Joint Control
Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations
🤖 AI Summary
To address the generality and efficiency bottlenecks in robot motion planning and control under complex environments, this paper proposes a dynamics-free, sampling-based Model Predictive Control (MPC) framework. The method integrates the fully parallel GPU-accelerated physics simulator IsaacGym seamlessly into the Model Predictive Path Integral (MPPI) controller, enabling direct optimization of control sequences via forward-dynamics sampling. This implicit treatment of contact dynamics and multi-joint coupling eliminates the need for explicit dynamical modeling. Crucially, the approach supports plug-and-play transfer across diverse robot morphologies and object types. We validate its superiority on challenging tasks—including mobile navigation with obstacle avoidance, non-prehensile manipulation, and high-dimensional whole-body control—demonstrating both high computational efficiency and successful sim-to-real deployment. The implementation is open-sourced.
📝 Abstract
We present a method for sampling-based model predictive control that makes use of a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI), that uses the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of a problem. By doing so, we eliminate the need for explicit encoding of robot dynamics and contacts with objects for MPPI. Since no explicit dynamic modeling is required, our method is easily extendable to different objects and robots and allows one to solve complex navigation and contact-rich tasks. We demonstrate the effectiveness of this method in several simulated and real-world settings, among which mobile navigation with collision avoidance, non-prehensile manipulation, and whole-body control for high-dimensional configuration spaces. This method is a powerful and accessible open-source tool to solve a large variety of contact-rich motion planning tasks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model Predictive Path Integral (MPPI)
GPU-accelerated physical simulation
Robotic motion planning
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