arXiv (All)
open access
RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effects. We study how repeated interaction can improve this capability without updating the base model. We introduce RoboHarn
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
- Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning#1117857
- FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation#1105524
- GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation#927056
- Benchmarking and Enhancing Skill-Level Memory for Partially Observable Robotic Manipulation#1143487
- ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations#998493
Record · ID 1143898
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