arXiv (All)
open access
VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation
Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. However, developing VTLA models is severely constrained by the massive amounts of vision-tactile data and computational resources required. To addres
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
- ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation#1012441
- InSight: Self-Guided Skill Acquisition via Steerable VLAs#1050899
- VisForce: Visual Grounding of Current and Desired Forces for Goal-Conditioned Dexterous Manipulation#1061417
- Spike-driven Vision-Language-Action Model#1144481
- CometVLA: Co-Training on an Embodied Data Pyramid towards Physical Understanding#799503
Record · ID 1037289
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