arXiv (All)
open access
DeltaWorld: Physically Consistent Interactive World Simulators via Action-Conditioned Latent Increment Learning
Interactive world simulators can provide scalable environments for robot planning, policy training, and evaluation by predicting action consequences while reducing reliance on repeated physical rollouts. To serve these applications, they must generate future image sequences that respond faithfully t
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
- Latent Action as Intention Enables Efficient Future Imagination for World Action Models#820636
- Reinforced Planning with Latent World Models#1118691
- MotionForge: A Data Generation Pipeline and Large-Scale Benchmark for Long-Horizon Manipulation of Dynamic Objects with Domain Shifts#1061324
- LIBERO-MAX: Do Robot Policies Adapt When the World Changes?#1131719
- X2Real: an eXtensive simulation benchmark for real-world generalist policies#1063660
Record · ID 1187005
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