arXiv (All)
open access
MIND: Instruction-Conditioned Incremental Grounding for Robots
Robots following persistent language instructions must decide which cached room judgments remain valid as their environment changes. We present MIND (Maintenance via INstruction-conditioned Dependencies), a framework for maintaining target-room decisions without re-evaluating every room after each e
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
- One Word, Different Action: A Real-Robot Benchmark for Language-Conditioned Embodied Reasoning#1084374
- How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism#998179
- World Action Planner: Generalizable Robot Decision-Making with Action-Conditioned World Models#1188290
- Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation#1035572
- Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing#1141658
Record · ID 1345085
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