On September 10th, Unitree introduced UnifoLM-WLA-1.0, which is a generative foundational model for next-generation humanoid robots with approximately 6 billion parameters. According to the company's statement, a single control file trained on data from a real robot spanning about 2500 hours can coordinate 64 tasks, covering 10 whole-body manipulations and 54 tabletop manipulations, as well as operate with both two-fingered grippers and multiple dexterous five-fingered hands.
This release differs from the previous demonstration of Unitree UnifoLM-X2 sparring; UnifoLM-WLA-1.0 represents a broader 'vision-language-action' stack for performing daily household and tabletop tasks, rather than a combat-oriented demonstration.
Architecturally, UnifoLM-WLA-1.0 is based on interaction-centric world modeling augmented by an MMDiT action expert. This embodied reasoning stage, named UnifoLM-ER-1, is built upon Qwen3-VL-4B and was trained on over 5 million embodied samples. These samples cover aspects such as point prediction, object detection, multi-image reasoning, 2D trajectories, 3D detection, and answering spatial questions, enhancing spatial perception while jointly training with general image and text data to maintain broad 'vision-language' skills.
Furthermore, dynamic region prediction utilizes optical flow to extract future scene changes, compresses them using VQ-VAE into fixed-length mask tokens, and trains the 'vision-language' model to predict which regions will start moving after interaction begins. Discrete action learning via residual vector quantization segments the unified action space into end-effector poses, arm joints, and lower body joints, aligns these tokens across common time steps, and feeds them into UnifoLM-ER-Flow. The WLA model they released includes the MMDiT expert for decoding continuous robot actions from this multimodal framework.
In spatial and embodied reasoning tests conducted on 16 multimodal datasets, Unitree reports that UnifoLM-ER-1-4B outperforms open models in seven benchmarks. Additionally, on overlapping spatial datasets, it surpasses the locally proprietary GPT-6 Astra in metrics such as Where2Place, BLINK spatial subtasks, CV-Bench, and EmbSpatial. Training relies on Unitree Open Datasets and BitRobot-HIW-500, as well as other real robot data sources. Video evaluations are demonstrated on Unitree G1 hardware performing tasks like folding towels, packing phones, making beds, sorting shoes, and cleaning bathrooms—tasks requiring coordinated movement of arms, end-effectors, and lower body within a single policy.
It is important to note that the project page is available to developers, but the code, models, and datasets are currently marked as 'Coming Soon.' Unitree has announced its intention to make the project open source and published documentation on the architecture and task matrix, but the weights and data corpora are not yet available for download. Until these resources become available, the practical signal for researchers and integrators is the disclosed roadmap of the general humanoid foundation and the benchmark table, rather than a ready-to-run control file for third-party robots.
