arXiv (All)
open access
Measuring the Stability Assumption Behind Action Chunking
Action chunking improves the performance of policies learned by behavioural cloning, and several mechanisms have been proposed to explain why, including temporal consistency, horizon reduction, representation learning, and reduced error compounding. We instead study what happens to an action error o
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
- Algebraic Consistency Alone Does Not Certify Temporal Structure in Latent Action Models#1049217
- MemBodied: Recurrent Associative Memory for Vision-Language-Action Models#1064800
- SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations#1145040
- Action Chunking Proximal Policy Optimization with Feedback Correction#1131092
- Reinforcement Learning for Real-Time Vision-Language-Action Policies#1011901
Record · ID 1156897
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