arXiv (All)
open access
Action Sequence Transfer via LLMs for Heterogeneous Environments
We present an action sequence transfer system that adaptively transfers user action sequences across different target spaces. Given an input action sequence from a source space and scene graph representations of both the source and target environments, our system predicts a corresponding action sequ
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
- Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer#1035913
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#927297
- GeomVLA: Unifying Scene, Motion, and Action in 3D#1011225
- GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation#975075
- MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models#363299
Record · ID 1128988
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