QuadVerse: 四足歩行シミュレーションのための視覚と物理の現実を統合するフレームワーク
QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation
RGB動画から再構成した3Dシーンを基盤に、視覚・接触・アクチュエータの各ギャップを統合的に補正し、実機データなしで視覚ナビゲーションをゼロショット展開できる四足歩行シミュレーションフレームワークを提案した。
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著者: Yuxiang Chen, Yuanhao Wang, Ziheng Zhang, Meng Zhang, Yu Liu, Yufei Jia, Tiancai Wang, Erjin Zhou, Jin Xie
分類: cs.RO
原文アブストラクト
Simulation is central to robot learning, yet the sim-to-real gap remains a major bottleneck. Existing approaches often tackle visual or dynamic gaps separately, overlooking how these individual mismatches accumulate and propagate throughout the robot's state evolution. In this paper, we introduce QuadVerse, an integrated framework that uses reconstructed scenes as a calibration substrate for aligning visual perception, physical interaction, and actuator dynamics. From captured RGB videos, we reconstruct geometry-constrained 3D Gaussian Splatting (3DGS) scenes that support batched photorealistic ego-view rendering and collision-ready semantic mesh extraction. The meshes further enable contact calibration by initializing spatially varying friction priors and refining them through trajectory-based posterior search. To address remaining actuator discrepancies, QuadVerse trains a residual dynamics compensator by replaying real-world trajectories on the contact-calibrated terrain, reducing the entanglement between terrain-induced contact errors and actuator non-idealities. Experiments show that QuadVerse improves reconstruction quality and locomotion tracking over relevant baselines. Leveraging this foundation, we demonstrate robust zero-shot visual-navigation policy deployment without task-specific real-world rollouts.
