EVO-WAM: Evolving World Action Models through Video-Action Verification

arXiv:2609.38057 · cs.CV, cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

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

Terminology

• Verification — 61× in this paper · explained in 2 episodes
• Consistency — 41× in this paper · explained in 3 episodes
• World Model — 19× in this paper · explained in 7 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• Verifier — 7× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Continual Learning — 2× in this paper · explained in 2 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Decoder — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Action-Conditioned World Model — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 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 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• Motus: A Unified Latent Action World Model
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• AdaWorld: Learning Adaptable World Models with Latent Actions
• GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation
• World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
• Ctrl-World: A Controllable Generative World Model for Robot Manipulation
• VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
• RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
• DreamGen: Unlocking Generalization in Robot Learning through Video World Models
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• OpenVLA: An Open-Source Vision-Language-Action Model
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
• Learning to Act from Actionless Videos through Dense Correspondences
• Causal World Modeling for Robot Control

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