arXiv (All)
open access
VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes from a multimodal demonstration distribution, conditioned on language and visual context. A wrong mode selection means exec
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
- IronMan: Information-Constrained Video-Action Learning for Robot Manipulation#1217696
- MAAP: Multi-Agent Active Perception for Collaborative Manipulation#1036318
- IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies#1084904
- HINT: Human-Intent Inception for Long-Horizon Robot Manipulation#973402
- VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models#1356642
Record · ID 1401826
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