arXiv (All)
open access
SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants
We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and semantics-aware active view-motion planning. Beyond inspecting uncertain observed regions, the scene graph explicitly hypothesizes unobserved peppe
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
- Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning#926474
- TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models#1062211
- TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models#1043149
- CoRe-WAM: Correspondence-Aligned Temporal Residuals for World Action Models#1063525
- FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation#1105524
Record · ID 821604
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