TMTPost publishes article on Gao Yuxiang and COCO Matrix

On August 31, 2026, TMTPost published an article about Gao Yuxiang and COCO Matrix.

Separate reports on robot learning have covered training data, simulation infrastructure and manufacturing equipment. It is important to treat this article as an interview and not equate the technical claims it contains with verified performance or deployment results.

This is the publication of an interview article about a company and an individual, not an announcement of a specific robot model or production equipment. Accordingly, statements or technical results in the article cannot be treated as independently verified performance or evidence of commercialization. This article does not reconstruct the interview’s arguments, but places it in the context of the broader landscape of robot learning described in related public reports. See the original article for details. Related reports cover training data, simulation and deployment on physical robots as separate efforts. NVIDIA announces GR00T 2 and Isaac Lab 3.0 at CoRL covered training data that included 100,000 hours of first-person-view data, along with frameworks for simulation and training. Report: Ant Lingbo open-sources tactile model “LingBot-Touch” reported the release of a tactile model. In addition, Skild AI releases “Steel,” a physics simulator that handles different robots and tasks simultaneously covered simulation. There is no confirmation that these efforts belong to the same company or a single technical framework, so the reports should not be linked as one product plan. The broader picture shows that discussions of robot adaptability must distinguish not only the model itself, but also the nature of the training data, validation in virtual environments and reproducibility on physical robots. The availability of public data and simulation infrastructure indicates that training and evaluation options are expanding, but does not by itself establish transferability across different robot platforms or reliability in real-world settings. Data collection conditions, task configurations and evaluation methods may also differ across reports, limiting straightforward performance comparisons. On the manufacturing side, Foundry Robotics raises $100 million to build an assembly automation factory reported a factory plan combining AI robot cells with manufacturing specialists. Training infrastructure and manufacturing capacity are separate issues, but when robot skills are transferred to actual production processes, equipment and process conditions must be considered alongside model evaluation. The publication of this article alone does not show that COCO Matrix uses these kinds of infrastructure or has a similar manufacturing setup. Going forward, it will be important to determine whether the reported technical claims are supported by official materials or reproducible evaluations, and whether the robot platforms, tasks and real-world settings covered are clearly specified.