arXiv (All)
open access
In-Context Robot Learning with VLM Agents
Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstrations can cover every task and situation a robot will encounter, making the ability to learn from context at deployment essential for generalization. Such
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
- CrossSafe: Towards Cross-Embodiment Latent Safety Filters#1076640
- Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding#924110
- Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding#919881
- Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones#1123080
- RoboTTT: Context Scaling for Robot Policies#373367
Record · ID 1013340
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