June 26, 2026
BEIJING, China – Striding AI today announced that it is developing a new generation of robot-based systems designed to accelerate the deployment of physical AI in real-world environments.
| Striding AI announces plans to develop next-generation robot infrastructure system for physical AI deployment |
The company’s approach focuses on building the foundational technology necessary for robots to perceive, reason, act, and continuously improve through interaction with the physical world. By integrating advanced foundational models with robot perception, control systems, real-world action data, and deployment infrastructure, Striding AI aims to enable intelligent machines to perform useful tasks in any commercial, industrial, or everyday environment.
“We believe that breakthroughs in physical AI will emerge from the continuous co-evolution of data, models, and infrastructure,” said Song Yao, Founder and CEO of Striding AI.
The company takes a systems-first approach to physical AI, integrating foundational models, robot hardware and software, data infrastructure, control systems, and deployment engineering to build scalable services. The company’s leadership team includes founders and executives with backgrounds in AI chips, autonomous driving, robotics research, and industrial technology, combining deep technical expertise with experience deploying complex technologies into production environments.
Striding AI will start with practical deployment scenarios in structured environments such as retail, where robots can support tasks such as replenishing shelves, counting inventory, organizing products, and assisting with checkout. These environments provide frequent human interactions, repeatable workflows, and rich operational data, making them a powerful starting point for developing scalable physical AI systems.
Striding AI expects its robot-based systems to be able to support a wide range of applications across sectors such as retail, food, agriculture, logistics, healthcare, and communications. From handling diverse objects and understanding retail shelves to planning and executing complex tasks, capabilities developed in real-world environments are part of integrated systems designed for a broader range of robotic applications.
In early internal testing, Striding AI’s human-involved RL method improved task success rates by up to 3x. To scale this flywheel, Striding AI is building infrastructure for robot pre-training, distributed reinforcement learning, and edge-to-cloud orchestration, creating a platform designed to improve as more robots operate in real-world environments.
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