VR-Robo：real2sim2real，机器人视觉强化学习导航和运动控制新范式！

Core Viewpoint - The article discusses the advancements in footed robot navigation and motion control through a unified framework called VR-Robo, which addresses the challenges of transferring learned strategies from simulation to real-world applications [3][16]. Related Work - Previous research has explored various methods to bridge the Sim-to-Real gap, but many rely on specific sensors and struggle to balance high-fidelity rendering with real geometric modeling [3][4]. Solution - The VR-Robo framework combines geometric priors from images to reconstruct consistent scenes, utilizes GS-mesh hybrid representation for creating interactive simulation environments, and employs neural reconstruction methods like NeRF for generating high-fidelity scene images [4][5][16]. Experimental Analysis - Comparative experiments were conducted against baseline methods, including imitation learning and textured mesh approaches, to evaluate the performance of the VR-Robo framework [11][12]. - Performance metrics reported include Success Rate (SR) and Average Reaching Time (ART), demonstrating VR-Robo's superior performance in various difficulty levels [14][15]. Summary and Limitations - VR-Robo successfully trains visual navigation strategies using only RGB images, enabling autonomous navigation in complex environments without additional sensors. However, it currently only applies to static indoor environments and has limitations in training efficiency and structural accuracy of the reconstructed meshes [16].