Rolling-WAM: World Action Models with Rolling Imagination

arXiv:2609.30247 · cs.RO, cs.AI, cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.RO, cs.AI, cs.CV

Submitted: 2026-09-24

Updated: 2026-10-05

Project page: https://rolling-wam.github.io

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 3× in this paper
• Conference on Robot Learning — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Native Video-Action Pretraining for Generalizable Robot Control
• InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
• $\omega$-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation
• Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models
• Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
• Flash-WAM: Modality-Aware Distillation for World Action Models
• Motubrain: An Advanced World Action Model for Robot Control
• AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
• Wan: Open and Advanced Large-Scale Video Generative Models
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving