arXiv (All)
open access
Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision
Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how the scene will evolve beyond the current observation can provide additional information for control. Action supervision alone does not explicitly enco
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
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#927297
- ActionGround: Training-Free Runtime Refinement of Frozen VLA Policies#1117968
- Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models#1297564
- MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies#1144291
- CARE: Certifying Acceleration for Vision-Language-Action Inference#1232956
Record · ID 1297288
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