arXiv (All)
open access
Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic Manipulation
Action chunking improves temporal coherence in visuomotor imitation learning by jointly predicting sequences of future actions. However, many existing policies regenerate entire action chunks even when errors are localized, potentially replacing otherwise valid predictions. We introduce Contextual M
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
- FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation#1105524
- Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks#1131313
- Streaming-WAM: Action-Conditioned World-Action Model for Asynchronous Robot Manipulation#1076583
- SAKI: Skill Assembly and Kinematic Imitation from Human Videos for Long-Horizon Mobile Manipulation#1130873
- WAVE-0: Bridging VLA Policies and WAMs with Lightweight Latent Future Prediction#1344069
Record · ID 1325760
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