VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egocentric images, language commands, and robot-compatible kinematic trajectories, yet no existing data source provides this complete tuple at scale. We address this bottleneck by generating vision-language-kinematics (VLK) supervision synthetically in reconstructed scenes. Our pipeline leverages 3D Gaussian Splatting to reconstruct metric-scale indoor environments, synthesizes navigation and object-interaction trajectories using privileged scene information, and renders paired egocentric observations after the fact...
| Authors | Yen-Jen Wang, Jiaman Li, Sirui Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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