arXiv (All)
open access
OmniVIC: A Self-Improving Variable Impedance Controller with Vision-Language In-Context Learning for Safe Robotic Manipulation
We present OmniVIC, a universal variable impedance controller (VIC) enhanced by a vision language model (VLM), which improves safety and adaptation in any contact-rich robotic manipulation task to enhance safe physical interaction. Traditional VIC have shown advantages when the robot physically inte
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
- Steerable and Reactive Grasping Through Modular Design with a Three-Point Interface#1051293
- SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation#1216242
- Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging#1128671
- FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation#1354522
- Effects of Robot-Assisted Upper Extremity Training on Cognitive and Physical Functions After Stroke#1443086
Record · ID 1490396
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