A new robotics data training center has opened in Beijing, marking a milestone for RealMan Robotics as it seeks to address persistent challenges in the robotics industry. The facility aims to advance technological capabilities and facilitate more robust AI model training by enabling access to diverse, real-world data. Attendees witnessed demonstrations that emphasized scenario-based testing, core research, and ecosystem collaboration. The aim is not only to support existing industries but also to promote the broader deployment of robots in daily life, reaching into sectors such as healthcare, retail, and manufacturing. Visitors to the center can observe a range of robots—including mobile manipulators, semi-humanoids on wheels, and robotic drone arms—actively engaged in lifelike task environments.
When compared to previous announcements from RealMan Robotics and similar centers in China, the new facility represents a shift from smaller pilot projects to an expanded, application-driven environment. Earlier efforts concentrated on simulation and laboratory-based data generation, often with lower numbers of robotic units and less environment diversity. The addition of over a hundred robots and multiple application domains sets a higher target for data quantity and variability. This expansion demonstrates a move toward supporting wider industry adoption through the development of standards and ecosystem ties.
How Does the Center Facilitate Data Generation?
Spanning approximately 3,000 square meters, the center has two distinct areas: a training zone and an application zone. Within these, 108 robots are performing tasks in ten constructed, realistic settings, from automotive assembly and eldercare to retail and catering, simulating conditions that closely match operational demands. This setup is projected to provide more than a million data points each year for use in advanced algorithm training.
What Problems are Being Tackled by RealMan Robotics?
The company aims to confront ongoing difficulties such as insufficient data generalization across scenarios, mismatches between simulations and reality, and poorly standardized data formats. By implementing a full-stack data collection and validation process, RealMan Robotics is working to bridge these gaps, thus potentially reducing deployment cycles for semi-humanoid robots and embodied AI. The company highlighted its intention to drive down operational costs while enhancing adaptability of robotic platforms in complex environments.
What Are the Goals and Industry Impact?
RealMan Robotics plans to promote cooperation between industry and academic partners through the center. A company representative stated,
“Robots face three enduring bottlenecks before they can scale into everyday life: operational capability, generalization, and cost efficiency.”
Building on this, the firm will encourage the sharing of technological resources and data, supporting business growth alongside product development. Eric Zheng, the Director of the Humanoid Robotics Data Training Center, noted during the launch,
“Traditional industrial arms are heavy and expensive, service robots remain too simplistic, and most lack the adaptability of humans in complex environments.”
The center is envisioned as both a hub for experimentation and a means to accelerate the integration of robots into commercial and consumer domains.
Continuous advancements in robotics require robust, high-quality datasets that represent true-to-life tasks and difficulties. This initiative by RealMan Robotics sets new benchmarks in this regard, contrasting with earlier data-generation projects which were smaller in scope or relied on controlled simulations. The inclusion of diverse robot types—dual-arm platforms, drone-arms, and quadrupeds—reflects an understanding of market needs where robotic solutions must adapt to varied scenarios. For businesses considering robotics integration, the availability of extensive, well-validated data increases the likelihood of operational success and reduces costly deployment errors.
