arXiv (All)
open access
EmbodiExp: Compositional Experience Transfer for Embodied Agents
Experience can help embodied agents turn high-level plans into grounded actions, but transferring it across tasks remains challenging: previously effective strategies may be inapplicable to the current state or fail to produce their intended outcomes. We present EmbodiExp, a framework for constructi
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
- EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training#1357146
- DialogMem: Evaluating Dialogue-Grounded Embodied Memory in 3D Environments#1389713
- ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop#200471
- The continuous dynamics of planning and acting in a virtually embodied task.#1415842
- MemTransfer: Benchmarking Memory Beyond Matched Experience in Embodied Decision-Making#1116644
Record · ID 1490250
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