ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
UnifoLM-WLA-1.0: One Model Driven, Whole-Body Coordination
Connects embodied understanding, interaction-centric world modeling and action learning for tabletop and whole-body humanoid manipulation.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
2026-09-28 · Base weights and fine-tuning release; earlier modules were released the same month
Why read this
Compare general multimodal capabilities with robot action interfaces, asking what data and training connect them.
What it connects
The official repository explicitly acknowledges building on starVLA and Qwen-Image.
Official code, weights and task descriptions
This is an official open-source project. Developer-reported capabilities are not independent replication or evidence of reliability in arbitrary environments.
Follow these connections ↗Sources for relations and versions
Citation
@misc{unitree2026unifolm,
title = {UnifoLM-WLA-1.0: One Model Driven, Whole-Body Coordination},
author = {Unitree},
year = {2026},
url = {https://github.com/unitreerobotics/unifolm-wla}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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