arXiv (All)
open access
STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images
Robots performing long-horizon visual manipulation observe high-dimensional images, but successful plans depend on action-relevant facts: what can be done now and what changes afterward. A useful planning representation should discard irrelevant visual details while preserving action applicability a
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
- GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models#1217487
- From Pixels to Executable Physics: Behavior-Grounded World Models for Embodied Action Consequence Prediction#1342842
- Resolving State-Representation Mismatch: State-Space Visual Reasoning for Open-Loop VLA Planning#1118124
- Memory as Plans: World-Action Modeling with Memory-Grounded Planning#999082
- EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies#1131581
Record · ID 1386298
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