QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation
For robot learning researchers, QuadVerse addresses the sim-to-real gap by jointly calibrating visual and physical mismatches, reducing the need for real-world data collection.
QuadVerse introduces an integrated framework that aligns visual perception, physical interaction, and actuator dynamics for quadruped simulation, achieving improved reconstruction quality and locomotion tracking, and enabling zero-shot visual-navigation policy deployment without task-specific real-world rollouts.
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.