arXiv (All)
open access
Achieve What You Imagined: Learning to Align Actions with Visual Plans
World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual predictions and the consequences implied by generated actions. We observe that WAMs can often generate visually plausible task-completion outcomes before produc
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
- FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models#1119435
- Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks#1131313
- Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models#1154482
- Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models#1136867
- Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer#1035913
Record · ID 1118905
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