88Z AI, 15 September — Unitree has fully open-sourced its new general humanoid foundation model, UnifoLM-WLA-1.0, releasing the code, model weights, and datasets simultaneously. Trained on large-scale multimodal perception and interaction-centric world modeling using approximately 2,500 hours of real-robot data, the model achieves leading performance in embodied reasoning benchmarks, rivaling top closed-source models. It enables a single model to manage 64 tasks, covering both desktop manipulation and whole-body mobil
At a glance
- UnifoLM-WLA-1.0 is fully open-source with code, model, and datasets, trained on 2,500 hours of real-robot data.
- A single model manages 64 tasks with cross-task and cross-end-effector generalization, rivaling top closed-source models.
The update
Unitree has officially launched and fully open-sourced its next-generation general humanoid foundation model, UnifoLM-WLA-1.0, making the code, model weights, and accompanying datasets publicly available simultaneously. The model's core training architecture is built on large-scale multimodal perception understanding and interaction-centric world modeling, optimized using approximately 2,500 hours of real-world robot operational data. In terms of performance, UnifoLM-WLA-1.0 leads among open-source models in various embodied intelligence reasoning benchmarks, with capabilities comparable to top-tier closed-source models in the industry. In practical applications, the model demonstrates strong generalization potential, capable of managing 64 distinct tasks through a single model. Its scenarios range from precise desktop manipulation to whole-body mobility control, supporting flexible adaptation across different task types and end-effectors.
Sources & editorial note
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