arXiv (All)
open access
Planning in the Backbone: DiffAdapterVLA for Native Continuous Trajectory Generation with Driving VLMs
Pretrained driving vision-language models (VLMs) integrate visual, route, language, and driving context into rich driving priors, yet their representation objectives remain separated from continuous driving planning. Existing methods typically begin trajectory generation only after the VLM has forme
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
- VTInstructor: Visual Trajectory Prompting for Navigation Instruction Generation in Continuous Environments#615259
- VTInstructor: Visual Trajectory Prompting for Navigation Instruction Generation in Continuous Environments#614292
- Temporal Forcing: 4D Representation Alignment for Vision-Language-Action Models#818701
- Temporal Forcing: 4D Representation Alignment for Vision-Language-Action Models#808709
- MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies#1144291
Record · ID 1011245
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