arXiv (All)
open access
Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks
World-Action Models (WAMs) improve robotic manipulation by conditioning action generation on predicted future observations, but future prediction adds further inference overhead to already expensive iterative action generation. Action chunking can amortize this cost over multiple actions, yet perfor
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
- MoWAM: Explicit Future Motion Prediction for Efficient World Action Models#1034507
- Streaming-WAM: Action-Conditioned World-Action Model for Asynchronous Robot Manipulation#1076583
- FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models#1119435
- FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation#1105524
- SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations#1145040
Record · ID 1131313
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