WorldLine: Action-Driven Visual Simulation for Robotic Manipulation

arXiv:2609.38059 · cs.RO, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/dexmal/opendw

Terminology

• Grounding — 16× in this paper · explained in 3 episodes
• Adaptation — 14× in this paper · explained in 1 episode
• Distillation — 14× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Accuracy — 11× in this paper · explained in 1 episode
• Regularization — 11× in this paper · explained in 3 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Distribution Matching — 6× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• World Model — 6× in this paper · explained in 7 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• International Conference on Robotics and Automation — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Domain Shift — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• Masked Visual Actions for Unified World Modeling
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Qwen3-VL Technical Report
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation
• Wan: Open and Advanced Large-Scale Video Generative Models
• RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
• RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation
• A Pragmatic VLA Foundation Model
• iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
• World Action Models are Zero-shot Policies
• PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation
• Action Images: End-to-End Policy Learning via Multiview Video Generation
• IRASim: A Fine-Grained World Model for Robot Manipulation
• Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation

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