arXiv AI
LePlanner: An Iterative Amortized Controller For World Models
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arXiv Machine Learning
Jun 8
Latent Geometry Beyond Search: Amortizing Planning in World Models
arXiv:2605. 08732v2 Announce Type: replace-cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging.
arXiv Machine Learning
Aug 20
Reinforced Planning with Latent World Models
Reinforced Planning with Latent World Models (RP1) is a novel method that learns to evaluate imagined outcomes via a critic and to improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. It is the first approach to fully learn plan improvement and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using far fewer roll‑outs and running up to 67× faster than the strongest alternative.
arXiv Machine Learning
Jun 18
Hierarchical Planning with Latent World Models
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
Hugging Face Trending Papers
Aug 19
Reinforced Planning with Latent World Models
Reinforced Planning with Latent World Models introduces RP1, a neural planner that learns to evaluate imagined outcomes via a critic and improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. Unlike existing planners that are hand‑designed or only inform policies, RP1 fully learns to refine plans and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using 1,000× fewer roll‑outs and up to 67× faster inference.
arXiv AI
Sep 30
Beyond a single latent space: a dual-latent world model for long-horizon planning
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
arXiv AI
1d ago
Hierarchical World Models with Implicit Dynamics
Hierarchical World Models with Implicit Dynamics (ID‑WM) introduces a two‑level approach that separates state‑space reachability from local control. A high‑level model samples feasible transitions across the state manifold, while a low‑level world model acts as an inverse dynamics solver to translate these transitions into precise actions. This design enables planners to identify reachable subgoals without being constrained by low‑level execution details, producing novel long‑horizon plans that outperform state‑of‑the‑art policy and diffusion‑based planners by 38.5% and single‑level MPC by 135% in complex, unseen configurations.