arXiv (All)
open access
ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation
General-purpose multimodal foundation models are increasingly capable of directly controlling robots, yet improving their manipulation capabilities through autonomous interaction remains challenging. A key challenge to such self-improvement is learning from failures in complex manipulation tasks inv
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
- FP2: Equipping Robotic Foundation Models with Force Control#1141755
- AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation#1037015
- PhysicsLENS: Diagnosing Physical Property Blindness in Video Generation Models#1156097
- ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation#1118664
- AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization#820670
Record · ID 1327064
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