arXiv (All)
open access
REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream. Event cameras provide microsecond temporal resolution and asynchronous sensing, but most learning-based methods accumulate events into frames or temporal bins, intr
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
- FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models#1119435
- TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models#1155834
- Event-based Optical Marker Systems: A survey#972914
- In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks#1142468
- Reinforcement Learning for Real-Time Vision-Language-Action Policies#1011901
Record · ID 1013804
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