arXiv (All)
open access
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation
Scaling robot manipulation models requires large amounts of interaction data, yet collecting diverse real-robot experience remains costly. Human manipulation videos, in contrast, provide abundant and diverse task experience that could potentially benefit robot learning. However, differences in morph
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- V2-STRep: VLM-Grounded Structured Task Representations for Reusable Robot Skills Acquired from Generated Videos#1034394
- Track, Articulate, Act: Generating Articulation from Casual Human Videos#1013325
- One-shot Human Video-Guided Adaptation for Vision-Language-Action Models#1354431
- ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training#819653
- ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training#809657
Record · ID 1357453
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive