arXiv (All)
open access
Intercepting the Future: Latent-Space Predictive World Model for Dynamic VLA Manipulation
Vision-Language-Action (VLA) models generalize across static manipulation but fail when objects move during task execution. They map the current observation to an action and assume the scene is stationary between observation and execution, so at any non-trivial object speed the resulting latency exc
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
- G$^3$VLA: Geometric Inductive Bias for Vision-Language-Action Models#1354932
- InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies#1064278
- RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation#1247464
- Imagine-RL: Residual-Confidence-Guided Cross-Attention for World-Model-Augmented VLA Reinforcement Learning#1050219
- FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs#1355001
Record · ID 1199869
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