QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation

• URL: http://arxiv.org/abs/2606.07118v2
• Date: Mon, 08 Jun 2026 03:25:26 GMT
• Title: QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation
• Abstract summary: We introduce QuadVerse, an integrated framework that uses reconstructed scenes as a calibration substrate for aligning visual perception, physical interaction, and actuator dynamics.<n>We show that QuadVerse improves reconstruction quality and locomotion tracking over relevant baselines.
• Score: 31.92788136725977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal [63.906929035961134]
  We open-source a reconstruction pipeline capturing 55 scene-aligned 4D human motion sequences in diverse, complex environments.<n>The resulting dataset comprises over 150k frames, which we evaluate against motion-capture ground truth.<n>Our pipeline enables community-driven dataset extensions and factors the problem to help researchers build context-aware robots.
  arXiv  Detail & Related papers  (2026-07-15T06:02:41Z)
• ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos [47.4010297818512]
  ArtiTwinSplat is a framework that automatically constructs articulated, photo-realistic digital twins of objects from RGB-D videos.<n>Our method is built on 3D Gaussian Splatting that preserve geometric fidelity and photometric realism.
  arXiv  Detail & Related papers  (2026-06-23T14:24:07Z)
• ManiSplat: Manipulation Trajectory Synthesis from Monocular Video via Decoupled 3D Gaussian Splatting [28.256286705954846]
  Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics.<n>We introduce ManiSplat, a unified framework that reconstructs controllable and decoupled Gaussian digital twins directly from robotic videos.<n>Our approach reconstructs interaction-driven dynamic scenes with high fidelity and controllability, effectively supporting downstream robotic tasks and policy learning.
  arXiv  Detail & Related papers  (2026-06-09T09:55:58Z)
• GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images [61.2170105836525]
  We propose an RGB-only framework that reformulates reconstruction as physically-grounded scene factorization and outputs a structured hybrid scene representation.<n>The resulting representation combines explicit rigid bodies with a decoupled background, enabling direct physics simulation while preserving visual realism.
  arXiv  Detail & Related papers  (2026-06-02T17:13:01Z)
• Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch [3.926587614210279]
  We propose a multimodal, physically grounded approach for metric-scale amodal object reconstruction and pose estimation under hand occlusion.<n>We leverage physical interaction signals: proprioception provides the posed hand geometry, and multi-contact touch constrains where the object surface must lie.
  arXiv  Detail & Related papers  (2026-04-10T08:32:51Z)
• FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos [134.8812117202239]
  We present FunRec, a method for reconstructing functional 3D digital twins of indoor scenes directly from egocentric RGB-D interaction videos.<n>We demonstrate applications on URDF/USD export for simulation, hand-guided affordance mapping and robot-scene interaction.
  arXiv  Detail & Related papers  (2026-04-07T09:22:12Z)
• Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation [53.141181758015186]
  We present Resonance4D, a physics-driven 4D dynamic simulation framework that couples 3D Gaussian Splatting with the Material Point Method.<n>Our key insight is that dynamic consistency can be enforced without dense temporal generation by jointly constraining motion in complementary domains.<n>We show that Resonance4D achieves strong physical fidelity and motion consistency while reducing peak GPU memory from over 35,GB to around 20,GB.
  arXiv  Detail & Related papers  (2026-04-02T13:00:22Z)
• HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions [69.43316445143408]
  We present HSImul3R, a unified framework for simulation-ready 3D reconstruction of human-scene interactions.<n>Existing methods suffer from a perception-simulation gap, leading to instability in physics engines and failure in embodied AI applications.
  arXiv  Detail & Related papers  (2026-03-16T17:58:33Z)
• Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow [61.297800738187355]
  Flow4R predicts a minimal per-pixel property set-3D point position, scene flow, pose weight, and confidence-from two-view inputs using a Vision Transformer.<n> trained jointly on static and dynamic datasets, Flow4R achieves state-of-the-art performance on 4D reconstruction and tracking tasks.
  arXiv  Detail & Related papers  (2026-02-15T06:58:08Z)
• Monocular Reconstruction of Neural Tactile Fields [14.002981599280787]
  We introduce neural tactile fields, a novel 3D representation that maps spatial locations to the expected tactile response upon contact.<n>Our model predicts these neural tactile fields from a single monocular RGB image -- the first method to do so.<n>When integrated with off-the-shelf path planners, neural tactile fields enable robots to generate paths that avoid high-resistance objects.
  arXiv  Detail & Related papers  (2026-02-13T01:25:19Z)
• 4DRadar-GS: Self-Supervised Dynamic Driving Scene Reconstruction with 4D Radar [15.713470339586058]
  We present a 4D Radar-augmented self-supervised 3D reconstruction framework tailored for dynamic driving scenes.<n>4DRadar-GS achieves state-of-the-art performance in dynamic driving scene 3D reconstruction.
  arXiv  Detail & Related papers  (2025-09-16T10:29:43Z)
• Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments [81.38641691636847]
  We rethink the problem of scene reconstruction from an embodied agent's perspective. We reconstruct an interactive scene using RGB-D data stream. This reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models.
  arXiv  Detail & Related papers  (2021-03-30T05:56:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.