arXiv (All)
open access
Accelerating VLA Inference with Perturbation Informed Execution
Action-chunking vision-language-action (VLA) models predict sequences of future robot actions. Under fixed-horizon execution, the robot executes a fixedlength prefix of each predicted chunk before invoking the policy again, leaving the remaining predictions unused. For a given number of executed act
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
- CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies#927297
- When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models#1201732
- Exploring the Limits of Combinatorial Generalization in Embodied Manipulation#1326675
- IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies#1084904
- Efficient Vision-Language-Action Management and Serving for Robot Factories#1000048
Record · ID 1356168
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