During a recent strategic partnership signing with Shangshi Technology, the Hong Kong-listed "first visual embodied intelligence stock" RECONOVA (07656.HK) announced plans to co-build an embodied intelligence super accelerator in Hong Kong. The collaboration aims to integrate capital, technology, platforms, and global resources to foster a full-chain ecosystem spanning technology incubation, product development, and real-world deployment. This initiative explores a novel "technology plus finance plus industry" synergy model, with RECONOVA contributing its robotics expertise to connect upstream and downstream partners.

Currently, the embodied intelligence sector is broadly divided into two main approaches: robotic body manufacturers focusing on motion control, engineering, and mass production, and general AI companies seeking to equip robots with advanced 'brains.' Both paths face a shared bottleneck—capability evolution lags behind the actual demands of general-purpose scenarios. RECONOVA pursues a third route, defining products based on specific scenarios, identifying which industrial tasks suit robotic execution, and then reverse-engineering the necessary perception, decision-making, and action capabilities. Rather than 'searching for scenarios with algorithms,' the company starts with practical B-end needs in airports and commercial spaces, tackling how machines 'see' first, then gradually extending along operational workflows to embodied robots while completing decision-making and execution abilities.

In communications with media, RECONOVA Chairman Zhan Donghui stated frankly that full commercial adoption of general-purpose humanoid robots will take at least a decade. He argued that purely technology-driven development fails to align with the growth rhythm of commercial tech enterprises, warning that companies fixated solely on ultimate generalization, lacking sustained commercial revenue, risk collapsing right before the industry's explosive phase. He emphasized RECONOVA's pragmatic approach, advocating that R&D must adhere to commercial principles, accelerate monetization pathways, and accumulate real-world data through constrained-scenario deployments to iteratively enhance general capabilities. This focused-landing strategy, he insisted, does not preclude achieving universality over time.

Zhan further predicted that the future robotics industry will inevitably witness labor division, with no single company completing all general-purpose technologies alone. Citing the airport luggage transfer robot already deployed, he noted that RECONOVA selects scenarios based on two key principles: first, adequate technological maturity for the environment; second, clear economic returns that customers can calculate and realize.

Airport luggage handling is widely recognized as a deep-water deployment zone due to variable bag sizes, materials, and shapes, alongside stringent human-machine mixed-operation safety standards. Instead of flashy stage demos, RECONOVA has integrated its robots directly into production workflows. Currently, the Xiaoyi luggage transfer robot has been connected to real flight baggage handling operations at a major hub airport in East China, undergoing routine POC validation. This system has established an '80-20' human-robot collaboration model, where roughly 80 percent of standard luggage is handled by robots, leaving the remaining 20 percent of soft bags, irregular items, and anomalies for human staff coordination.