VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion

• URL: http://arxiv.org/abs/2502.01536v1
• Date: Mon, 03 Feb 2025 17:15:05 GMT
• Title: VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion
• Authors: Shaoting Zhu, Linzhan Mou, Derun Li, Baijun Ye, Runhan Huang, Hang Zhao,
• Abstract summary: This paper presents a Real-to-Sim-to-Real framework that generates and physically interactive "digital twin" simulation environments for visual navigation and locomotion learning.
• Score: 25.440573256776133
• License:
• Abstract: Recent success in legged robot locomotion is attributed to the integration of reinforcement learning and physical simulators. However, these policies often encounter challenges when deployed in real-world environments due to sim-to-real gaps, as simulators typically fail to replicate visual realism and complex real-world geometry. Moreover, the lack of realistic visual rendering limits the ability of these policies to support high-level tasks requiring RGB-based perception like ego-centric navigation. This paper presents a Real-to-Sim-to-Real framework that generates photorealistic and physically interactive "digital twin" simulation environments for visual navigation and locomotion learning. Our approach leverages 3D Gaussian Splatting (3DGS) based scene reconstruction from multi-view images and integrates these environments into simulations that support ego-centric visual perception and mesh-based physical interactions. To demonstrate its effectiveness, we train a reinforcement learning policy within the simulator to perform a visual goal-tracking task. Extensive experiments show that our framework achieves RGB-only sim-to-real policy transfer. Additionally, our framework facilitates the rapid adaptation of robot policies with effective exploration capability in complex new environments, highlighting its potential for applications in households and factories.

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