Beijing has launched a three-year action plan to build an embodied AI industrial ecosystem by 2027, accelerating the commercial deployment of robotics in retail and logistics. The initiative follows a national strategy to cultivate advanced robotics as a pillar industry, backed by a city-wide technology contract transaction value that reached 1 trillion yuan last year.
As a primary center for technological innovation, Beijing holds a distinct advantage in developing embodied intelligence. The city hosts more than 2,500 AI enterprises and maintains the largest concentration of large AI models in the country. According to Zhai Tianrui, deputy director of the Beijing Municipal Science and Technology Commission, this extensive infrastructure supports both research and industrial deployment of robotics.
Real-world deployment in retail and logistics
The Galbot G1 humanoid robot is now operating daily at a FamilyMart store in Zhongguancun, demonstrating embodied intelligence in a commercial setting. Responding to voice commands, it retrieves drinks and snacks for customers. This marks the first time an embodied intelligence robot has entered continuous service in a global convenience store, moving beyond initial demonstrations.
The system handles high-frequency retail tasks, managing items of varying shapes, temperatures, and packaging to reduce staff workload. This practical application in physical environments directly informs AI for Operations, where continuous feedback loops improve real-world workflow automation.
Galbot co-founder Zhang Zhizheng said the robot relies on the company's self-developed AstraBrain system. This architecture integrates task planning, motion control, and dexterous manipulation. Supported by an AstraSynth data pyramid combining internet, human behavioral, simulation, and real-world feedback data, the system enables real-time intent understanding and action execution.
Overcoming generalization in unstructured environments
A primary technical hurdle in embodied intelligence is generalization, which requires robots to perform diverse tasks reliably in unpredictable physical spaces. Developers are addressing this by applying large-scale local datasets and self-developed AI models.
"In the past, robots could only perform predefined tasks in structured environments," the company said. "Now, embodied intelligence robots can adjust strategies in real time across different scenarios. They truly have a 'brain.'"
Logistics has also become a priority for large-scale deployment. At a recent exhibition, Beijing-based Robot Era demonstrated its M7 sorting robot, which identifies parcels on conveyor belts and dynamically adjusts its grasping actions. The robot processes up to 1,200 parcels per hour, with operational data feeding back into model training to improve accuracy.
These advancements in model training and system architecture are core components of AI for IT & Development, as engineers build systems that require continuous data ingestion and refinement. Robot Era co-founder Chen Jianyu said logistics was chosen for commercialization due to strong demand for intelligent upgrades and a vast market scale.
The company has secured roughly 1,000 unit orders and formed partnerships with major operators including China Post and SF Express. The robots currently operate across more than 10 logistics centers in five provinces and cities nationwide, with sorting efficiency reaching up to 90 percent of human-level performance in some scenarios.
Why this matters for IT and development professionals
The shift from structured, predefined robotic tasks to generalized, end-to-end embodied AI requires a fundamental change in software architecture. Developers must now design systems that integrate large-scale multimodal data, real-time sensor feedback, and continuous model training loops. Understanding how architectures separate task planning from motion control provides a blueprint for building resilient autonomous systems that operate outside controlled laboratory environments.
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