VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Score
55Why selected
The method is innovative, addressing the critical data bottleneck with 3D Gaussian Splatting and synthetic data generation, with solid experimental results on a real robot and open-source code, showing high practical value.
AI commentary
「This work addresses the data scarcity bottleneck for perception-based humanoid manipulation, achieving effective sim-to-real transfer via purely synthetic data, offering a new paradigm for low-cost, scalable robot learning.」
Quick overview
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Existing data sources cannot provide the complete vision-language-kinematics tuple at scale. VLK reconstructs indoor scenes via 3D Gaussian Splatting, synthesizes navigation and object-interaction trajectories, generating 48,000 paired trajectories without human intervention. The trained policy successfully performs navigation and object transport on a physical Unitree G1 robot, demonstrating that synthetic interactions in reconstructed scenes provide effective supervision for sim-to-real perception-based humanoid loco-manipulation.
- Proposes VLK framework to generate vision-language-kinematics supervision synthetically in reconstructed scenes.
- Uses 3D Gaussian Splatting to reconstruct metric-scale indoor environments and synthesize navigation and interaction trajectories.
- Produces 48,000 paired trajectories with no human intervention.
- Validated on a physical Unitree G1 robot for navigation and single-object transport tasks.
Full text
Computer Science > Robotics
arXiv:2606.30645v1 (cs) [Submitted on 29 Jun 2026]Title:VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Authors:Yen-Jen Wang, Jiaman Li, Sirui Chen, Takara E. Truong, Pei Xu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, Karen Liu View a PDF of the paper titled VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes, by Yen-Jen Wang and 11 other authors View PDF HTML (experimental)Abstract: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. We produce 48,000 paired trajectories with no human intervention and train a VLK policy that predicts short-horizon whole-body kinematic trajectories. A whole-body tracker converts these predictions into actions on the physical humanoid. We evaluate on the physical Unitree G1 performing navigation and single-object transport, demonstrating that synthesized interactions in reconstructed scenes provide effective supervision for sim-to-real perception-based humanoid loco-manipulation. Project Website: this https URL
| Comments: | 19 pages, 7 figures, 4 tables |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Graphics (cs.GR); Systems and Control (eess.SY) |
| Cite as: | arXiv:2606.30645 [cs.RO] |
| (or arXiv:2606.30645v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30645 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Yen-Jen Wang [view email][v1] Mon, 29 Jun 2026 17:59:55 UTC (6,941 KB)
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