arXiv (All)
open access
Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation
Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. Iterative generative policies can achieve higher task success rates, but repeated network evaluations increase inference latency. Refining action pro
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
- SPIDER: Scalable Physics-Informed Dexterous Retargeting#1116159
- ReAT: Residual Action Tokenization for Autoregressive Robot Policies#1297445
- ReForce: Learning Force-aware Retargeting for Dexterous Manipulation#616717
- ReForce: Learning Force-aware Retargeting for Dexterous Manipulation#616217
- Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control#1034388
Record · ID 1354450
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