arXiv (All)
open access
Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress
In Vision-Language Navigation (VLN) tasks involving multi-step instructions as guidance, an agent must associate its observations with the relevant instruction content at each decision point. Most graph-based methods commonly match candidate trajectories with the entire instruction, allowing already
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
- Stable and Efficient Real-World Online VLA Post-Training via Asynchronous Replay-Anchored Policy Improvement#1037571
- Visual Access Boundaries in Vision-Language Model Reasoning#366303
- Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment#138984
- HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models#1052126
- VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models#615039
Record · ID 1297456
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