An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer with Differentiable Simulation

• URL: http://arxiv.org/abs/2503.10118v1
• Date: Thu, 13 Mar 2025 07:27:05 GMT
• Title: An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer with Differentiable Simulation
• Authors: Lu Shi, Yuxuan Xu, Shiyu Wang, Jinhao Huang, Wenhao Zhao, Yufei Jia, Zike Yan, Weibin Gu, Guyue Zhou,
• Abstract summary: This paper introduces a novel Real-Sim-Real loop framework to address the gap between simulation and real-world conditions.<n>A key contribution of our work is the design of an informative cost function that encourages the collection of diverse and representative real-world data.<n>Our approach is implemented on the versatile Mujoco MJX platform, and our framework is compatible with a wide range of robotic systems.
• Score: 13.15220962477623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. This paper introduces a novel Real-Sim-Real (RSR) loop framework leveraging differentiable simulation to address this gap by iteratively refining simulation parameters, aligning them with real-world conditions, and enabling robust and efficient policy transfer. A key contribution of our work is the design of an informative cost function that encourages the collection of diverse and representative real-world data, minimizing bias and maximizing the utility of each data point for simulation refinement. This cost function integrates seamlessly into existing reinforcement learning algorithms (e.g., PPO, SAC) and ensures a balanced exploration of critical regions in the real domain. Furthermore, our approach is implemented on the versatile Mujoco MJX platform, and our framework is compatible with a wide range of robotic systems. Experimental results on several robotic manipulation tasks demonstrate that our method significantly reduces the sim-to-real gap, achieving high task performance and generalizability across diverse scenarios of both explicit and implicit environmental uncertainties.

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