As embodied AI moves from technical concept to real-world deployment, the industry is entering a more decisive stage. The question is no longer only whether robots can move, but whether they can truly understand environments, adapt to scenarios, and complete stable, repeatable commercialization.

Today, Mr. Zhang Feng, founder of Union Image, was invited to attend the 2026 AI Partner AI+ Industry Conference Hangzhou Session and the "Meet Hangzhou, Connect Fuchun" Embodied AI Industry Chain Matchmaking Event. He joined the roundtable discussion on "Building an Embodied AI Ecosystem: Upstream-Downstream Collaboration and Commercialization Paths."

The roundtable brought together companies from new energy manufacturing, visual perception, exoskeleton robotics, robotics system platforms, kitchen automation, and embodied AI ecosystem operations to discuss the key challenges from technical validation to industrial deployment.

Embodied AI Is Entering the Deep-Water Zone of Deployment

Over the past period, embodied AI has become a core topic across artificial intelligence and robotics. From humanoid robots and industrial robots to service robots, exoskeleton devices and kitchen robotics, the industry is actively exploring how AI enters the physical world and how robots can understand space, execute tasks and serve people.

Unlike pure software or internet products, embodied AI cannot be commercialized through a single technical breakthrough alone. It requires coordinated capabilities across hardware, algorithms, data, sensors, supply chains, scene understanding and engineering delivery. A weakness in any link may affect product stability, user experience and commercialization efficiency.

In this sense, the development of embodied AI is fundamentally a competition in industry-chain collaboration.

From Seeing to Understanding: Vision Systems Are the Key Entry Point

During the roundtable, Union Image focused on visual perception capabilities for embodied AI scenarios. Many people assume that once a robot is equipped with a camera, it can already "see." For robots operating in complex physical environments, however, seeing is only the first step.

Robots need more than image capture. They must recognize objects, understand space, estimate distance, perceive dynamic changes and convert visual information into actionable decision-making signals. In other words, the vision systems required by embodied AI are evolving from traditional image acquisition toward deeper environmental understanding.

This involves depth cameras, edge AI vision, high-speed visual transmission, image processing, algorithm adaptation and system integration. It also raises higher requirements for stability, real-time performance, power consumption, size, cost and mass-production consistency.

From Demo to Productization, Vision Must Survive Real-World Validation

The embodied AI industry has already produced many impressive demos and concept showcases. But moving from a demo to a deliverable, mass-producible and long-running product still requires crossing multiple thresholds.

For vision systems, the challenge is especially clear: recognition in a lab environment does not guarantee stability under complex lighting, reflective materials and dynamic scenes; success on a single prototype does not guarantee consistency in scaled production; and a short-term demonstration does not prove long-term low-latency, high-reliability performance in real applications.

Therefore, real industrial deployment of embodied AI must be evaluated at the system level, not only by single-point performance. This is also the direction Union Image has long focused on: building integrated capabilities from visual hardware and image tuning to edge AI, system integration and mass-production delivery for robotics, intelligent terminals, industrial automation and smart wearable scenarios.

Industry Partners Discuss Commercialization Paths

The roundtable guests represented different parts of the embodied AI value chain, including new energy manufacturing, medical rehabilitation, kitchen scenarios, industrial systems, robotics platforms and ecosystem services. The discussion therefore focused not only on technology itself, but also on how technology connects with real demand.

What practical value can humanoid robots create on industrial production lines? Are exoskeleton devices replacing people or enhancing human capability? How can robotics system platforms support embodied AI product deployment? Why have high-frequency, complex and fragmented scenarios such as kitchens not yet been transformed by robots at scale? What barriers must be crossed before embodied AI enters everyday life?

These are the core questions the industry must face as it moves from popularity to practical deployment.

Making Vision a Foundational Capability for Embodied AI

The future of embodied AI will not be determined by a single technology. It requires upstream core components, perception systems, algorithm platforms, robot OEMs, scenario owners, supply chains and ecosystem partners to work together.

In this process, vision systems will continue to play a critical role as the entry point for robots to understand the world: from seeing objects to understanding space, from capturing images to supporting decisions, and from technical validation to product delivery.

Union Image will continue to focus on AI vision and embodied intelligence scenarios, helping visual capability evolve from standalone hardware into systematic solutions, and from laboratory demonstrations into real-world applications. As the industry chain matures, embodied AI will gradually move from exhibition-stage imagination into factories, homes, service environments and broader physical spaces. Vision is the first step for robots to understand the world.

Vision for All. Vision for Robotics.