What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
cs.AI, cs.LG, cs.RO, stat.ML
Submitted: 2025-12-30
Updated: 2026-09-02
Code: https://github.com/facebookresearch/jepa-wms
Terminology
Sources
- Cosmos World Foundation Model Platform for Physical AI
- Combating the Compounding-Error Problem with a Multi-step Model
- Back to the Features: DINO as a Foundation for Video World Models
- VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- D4RL: Datasets for Deep Data-Driven Reinforcement Learning
- Embodied AI Agents: Modeling the World
- The CMA Evolution Strategy: A Tutorial
- GAIA-1: A Generative World Model for Autonomous Driving
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Closing the Train-Test Gap in World Models for Gradient-Based Planning
- On the difficulty of training Recurrent Neural Networks
- A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
- Proximal Policy Optimization Algorithms
- DINOv3
- A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
- Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models
- DDP-WM: Disentangled Dynamics Prediction for Efficient World Models
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