arXiv (All)
open access
Learning Reflexive Behavior for Contact-Rich Manipulation
During contact-rich manipulation, interactions between a robot and its environment carry information about local geometry: a surface prevents penetration, a bore guides a peg. A controller that exploits these interactions can comply with environmental constraints while preserving task intent. Robot-
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
- Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration#1344338
- ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation#1131482
- Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation#1342039
- SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation#1216242
- EgoCPT: Learning Dexterous Manipulation from a Single Ego-Centric Video with Contact-Aware Optimization#1342786
Record · ID 1187125
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