VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Summary
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, y...
Original Article
Captured source content or English translation, normalized into this reading format.
arXiv is now an independent nonprofit!Learn more×
Search arXiv
Press Enter to search ·Advanced search
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
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] | | | (orarXiv:2606.30645v1[cs.RO] for this version) | | |https://doi.org/10.48550/arXiv.2606.30645<br>Focus to learn more<br>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)
Full-text links:
Access Paper:
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
Current browse context:
cs.RO
[< prev](https://arxiv.org/prevnext?id=2606.30645&function=prev&context=cs.RO "previous in cs.RO (accesskey p)") \| [next >](https://arxiv.org/prevnext?id=2606.30645&function=next&context=cs.RO "next in cs.RO (accesskey n)")
Change to browse by:
References & Citations
export BibTeX citation
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer _(What is the Explorer?)_
Connected Papers Toggle
Connected Papers _(What is Connected Papers?)_
Litmaps Toggle
Litmaps _(What is Litmaps?)_
scite.ai Toggle
scite Smart Citations _(What are Smart Citations?)_
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv _(What is alphaXiv?)_
Links to Code Toggle
CatalyzeX Code Finder for Papers _(What is CatalyzeX?)_
DagsHub Toggle
DagsHub _(What is DagsHub?)_
GotitPub Toggle
Gotit.pub _(What is GotitPub?)_
Huggingface Toggle
Hugging Face _(What is Huggingface?)_
ScienceCast Toggle
ScienceCast _(What is ScienceCast?)_
Demos
Demos
Replicate Toggle
Replicate _(What is Replicate?)_
Spaces Toggle
Hugging Face Spaces _(What is Spaces?)_
Spaces Toggle
TXYZ.AI _(What is TXYZ.AI?)_
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower _(What are Influence Flowers?)_
Core recommender toggle
CORE Recommender _(What is CORE?)_
- Author
- Venue
- Institution
- Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.
Which authors of this paper are endorsers?\| Disable MathJax (What is MathJax?)
Region
Global
Heat Score
73
Category
Policy
Language
en