What Must a World Model Distinguish for Planning?
cs.LG, cs.RO
Submitted: 2026-09-26
Updated: 2026-09-26
Terminology
Sources
- Deep Variational Information Bottleneck
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
- Metrics for Finite Markov Decision Processes
- World Models
- Dream to Control: Learning Behaviors by Latent Imagination
- Temporal Difference Learning for Model Predictive Control
- Representation Learning in Deep RL via Discrete Information Bottleneck
- Objective Mismatch in Model-based Reinforcement Learning
- DINOv2: Learning Robust Visual Features without Supervision
- ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
- Motubrain: An Advanced World Action Model for Robot Control
- What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
- Physically Viable World Models: A Case for Query-Conditioned Embodied AI
- World Action Models are Zero-shot Policies
- Learning Invariant Representations for Reinforcement Learning without Reconstruction
- World Action Planner: Generalizable Robot Decision-Making with Action-Conditioned World Models
- DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
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