arXiv (All)
open access
LEAP: Learning Emergent Active Perception for Quadruped Navigation
Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment. Learned systems typically encourage this behavior with hand-designed proxy objectives, such a
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
- D$^2$-VLA: Dual-Memory Dual-Frequency Vision-Language-Action Model For Long Dynamic Manipulation#1129410
- Towards High-DoF Dexterous Manipulation through VLA Post-Training#1014254
- TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning#793774
- TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning#789769
- Efficient robot navigation inspired by honeybee learning flights.#239607
Record · ID 1011359
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