宇树：开源机器人世界大模型！

Core Viewpoint - The article discusses the release of a new open-source model named UnifoLM-WMA-0, which is designed to enhance the interaction between robots and their environments through a world model that understands physical laws [1][9]. Group 1: Model Performance - The model demonstrates effective performance in tasks such as stacking blocks, with predictions closely matching actual operations [3]. - It can also handle more intricate tasks, such as organizing stationery, showcasing its versatility [7]. Group 2: Model Features - UnifoLM-WMA-0 is part of the UnifoLM series, specifically tailored for general robot learning and adaptable to various robotic platforms [9]. - The model's training code, inference code, and checkpoints have been fully open-sourced, quickly gaining over 100 stars on GitHub [11]. Group 3: Training Strategy - The training strategy involved fine-tuning a video generation model using the Open-X dataset to adapt its capabilities to real-world robotic tasks [15]. - The model operates under a dual-function architecture: a decision mode for predicting key information during physical interactions and a simulation mode for generating realistic environmental feedback based on robot actions [20]. Group 4: Dataset Utilization - The training utilized five open-source datasets provided by Unitree Technology, which contributed to the comprehensive training process [22]. - The model excels as a simulation engine, capable of generating controlled interactions based on current scene images and future action commands [23].