arXiv (All)
open access
Topology-Informed Visual Prompting For Vision Language Action Policies
Vision-language-action (VLA) policies can struggle with manipulation tasks with complex obstacle geometries due to partial observability. These complex geometries can lead to similar visual observations or robot configurations requiring qualitatively different actions, a distinction that can be quan
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
- InSight: Self-Guided Skill Acquisition via Steerable VLAs#1050899
- InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies#1064278
- IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies#1084904
- A Survey on Efficient Vision-Language-Action Models#1129131
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics#382879
Record · ID 1050133
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