InternW0-: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
cs.RO, cs.CV
Submitted: 2026-09-25
Updated: 2026-09-25
Code: https://github.com/OpenDriveLab/AgiBot-World
Project page: https://internrobotics.github.io/InternW0-Delta
Terminology
Sources
- Scalable Behavior Cloning with Open Data, Training, and Evaluation
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- Revisiting Feature Prediction for Learning Visual Representations from Video
- Motus: A Unified Latent Action World Model
- Real-Time Execution of Action Chunking Flow Policies
- Training-Time Action Conditioning for Efficient Real-Time Chunking
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- LeRobot: An Open-Source Library for End-to-End Robot Learning
- AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
- InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
- SAM 3: Segment Anything with Concepts
- ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
- RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- A Definition and Roadmap for World Models
- RynnBrain: Open Embodied Foundation Models
- MolmoAct2: Action Reasoning Models for Real-world Deployment
- EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
- Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
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- 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