arXiv (All)
open access
ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation
Reliable robotic grasping benefits from estimating the evolving physical interaction and selecting a grasp-dependent compression target. Tactile sensors provide direct interaction measurements but require dedicated hardware at deployment. We introduce ZeroTouch, a tactile-supervised framework that p
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
- ReTouch: Empowering Contact-Rich Dexterous Manipulation with Online-Refined Tactile Prediction#630096
- ReTouch: Empowering Contact-Rich Dexterous Manipulation with Online-Refined Tactile Prediction#628234
- TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies#1105528
- HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing#1050079
- Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly#1012331
Record · ID 1036116
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