arXiv (All)
open access
Flow-based Policy Adaptation without Policy Updates
Foundation models and other policies trained on large and diverse datasets can endow robots with semantic and task knowledge that would be difficult to acquire from task-specific demonstrations alone. Despite their broad scene understanding and reasoning capabilities, however, these models often lac
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
- Steertuning Policies from a Few Human Videos#1344209
- Steertuning Policies from a Few Human Videos#1319065
- DATAFARM: Distribution-Aligned Task and Motion Planning for Fine-Tuning Vision-Language-Action Models#1000274
- RoboHarness: A Simple Harness Outperforms VLA and World Actions Models#1324619
- ComManip: Overfitting Manipulation Policies to Comfortable Regions#1132129
Record · ID 1324520
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