arXiv (All)
open access
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this coupled behavior from limited demonstrations remains challenging. We propose See2Act, an imitation learning approach th
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
- An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning#1117093
- EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies#1077239
- Object-centric diffusion policies for real-world robotic-arm imitation learning.#415380
- MemBodied: Recurrent Associative Memory for Vision-Language-Action Models#1064800
- Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control#1234747
Record · ID 1327426
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