arXiv (All)
open access
RoboCap: A New Platform for Egocentric Robot Learning
Despite its promise for scaling robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integrating ergonomic hardware with centimeter-precise 3D algorithms, at a precision that has not been publicly demonstrated. To address this gap, we introduce
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
- EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos#1202146
- Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances#974091
- DESIGN AND EXPERIMENTAL EVALUATION OF A CUSTOM VISION-GUIDED APPROACH TO 2D PART LOCALIZATION FOR INDUSTRIAL ROBOTS#1060966
- Adaptive World Memory 3D Foundation Model for Scalable 3D Mapping, Localization, and Rendering#1035902
- AttenA+: Rectifying Action Inequality in Robotic Foundation Models#180711
Record · ID 1216592
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