arXiv (All)
open access
Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation
Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-aware manipulation policies have shown promise, they still face several limitations: (i) reliance on multi-view observations during inference, which is i
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
- MAAP: Multi-Agent Active Perception for Collaborative Manipulation#1036318
- HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos#1110251
- TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views#822318
- See like a Robot: Robot-Centric Pointmaps for VLA Models#1049919
- AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation#798967
Record · ID 1117313
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