arXiv (All)
open access
WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation
Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations, retries, and pauses. Prior frame-level progress reward models supervise on absolute temporal progress proxies that suffer from label noise, or require
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
- SPOT: Spatial Perception-Oriented Long-Horizon Humanoid Teleoperation#985340
- Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds#1050840
- Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Object Category-agnostic Temporal Localization#1048693
- GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation#780772
- Towards VLA-Dreamer: Refining VLA Behavior Using World Models#1105871
Record · ID 974474
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