Breaking Through with Deliberate Evolution: The Symbiotic Path of Construction Robots and Embedded Development in the AI Era. After listening to Professor Wang Yuquan’s live session on “Deliberate Evolution – A Guide to Success in the AI Era”, my deepest realization is that under the wave of AI, “passive adaptation” can only be eliminated. Only by actively anchoring the integration points of technology and industry can we achieve true evolution. As a practitioner deeply engaged in the field of construction robots for many years, I increasingly realize that the deep binding of embedded development and AI is the core path of “deliberate evolution” in the construction robot industry and the key for us practitioners to break through our own limitations. When discussing the integration of AI and industry, Professor Wang Yuquan’s concept of the “expert model” precisely hits the pain points in the field of construction robots. Previously, our embedded development for construction robots mostly stayed at the level of “hardware driving functions”—for example, enabling transport robots to move along preset trajectories and allowing assembly robots to perform fixed actions, which essentially made them “automated devices” rather than “intelligent partners”. However, the “expert model” has given us a new direction: we have begun to shift the focus of embedded development from “single hardware function realization” to “collaboration between hardware and AI expert models”. For instance, in the fault diagnosis phase, by using embedded sensors to collect real-time data on the robot’s motor speed, battery voltage, and mechanical arm operating resistance, we can connect to a trained “fault diagnosis expert model” that allows the robot to instantly identify issues such as “motor wear” and “poor circuit contact”, just like a maintenance engineer with ten years of experience, and even predict component wear risks in advance. This integration has transformed embedded development from being an “isolated technical profession” into a bridge connecting hardware and AI capabilities, enabling construction robots to truly possess the core competitiveness of “intelligence”. Professor Wang Yuquan also mentioned that current AI hardware is mostly at a “toy level” and will inevitably upgrade to “intelligent necessities” in the future, which is particularly evident in the embedded hardware supporting construction robots. In the past, the environmental sensors we equipped for construction robots could only simply collect basic data such as temperature, humidity, and distance, with data analysis and decision-making relying entirely on manual input; remote control terminals were merely “command transmitters” and could not provide effective assistance to operators. However, with the penetration of AI technology, we have begun to iterate on embedded hardware: optimizing the embedded computing modules of sensors so that they can not only collect data but also analyze it in real-time through lightweight AI algorithms—such as in high-altitude operation robots, where sensors can quickly distinguish between “construction personnel”, “building materials”, and “danger zones” through AI visual recognition, and immediately send warning signals to the control terminal via the embedded system; the control terminal has also integrated a “path planning AI module”, allowing operators to simply input construction goals, and the terminal can automatically generate the optimal path based on on-site data, even suggesting actions like “avoid obstacles” and “adjust mechanical arm angles”. Behind these changes is a shift in the embedded development mindset: no longer pursuing “hardware parameter accumulation”, but focusing on the “adaptation of AI capabilities”, making each embedded hardware an important component of the “intelligent necessities” of construction robots. In the context of global competition, Professor Wang Yuquan emphasized that “relying solely on hardware upgrades is difficult to gain an advantage; human-machine collaboration is the key to breaking through”, a point we have deeply experienced in multinational construction projects. Previously, our construction robots faced challenges in overseas projects due to “slow adaptation to construction scenarios” and “low equipment scheduling efficiency”—the construction environments and material specifications on overseas sites differ significantly from those in China, and traditional embedded hardware could not adapt quickly, with manual parameter adjustments being time-consuming and prone to errors. Later, we restructured the system through “embedded development + AI collaboration”: adding a “scene adaptation AI module” to the embedded hardware, allowing the robot to quickly collect on-site data and autonomously adjust construction parameters upon arriving at a new site; simultaneously, the embedded system uploads the operational data of multiple robots in real-time to a cloud-based AI scheduling platform, which automatically assigns tasks based on construction progress and equipment status—such as when “Transport Robot A” completes material transportation, the AI scheduling system immediately instructs “Assembly Robot B” to start assembly operations without manual intervention. In this model, embedded development becomes the foundation of “human-machine collaboration”: hardware is responsible for “data collection and command execution”, while AI handles “analysis and decision-making”. The combination of the two not only solves the adaptation challenges of multinational projects but also improves construction efficiency by over 40%, allowing our products to establish a foothold in global competition. Regarding personal development, the “Marshal Thinking” advocated by Professor Wang Yuquan is particularly important for embedded development practitioners. In the past, we often thought that “writing more concise embedded code” and “debugging hardware more accurately” were core competitive advantages, but the “Marshal Thinking” in the AI era tells us: we cannot just focus on “technical details”; we must also pay attention to “how technology serves industry needs”. For example, during product upgrades, we no longer first consider “what functions embedded hardware can achieve”, but rather analyze the pain points of construction through AI—such as “high risks in high-altitude operations” and “low efficiency in repetitive labor”—and then think about “how to solve these pain points through the integration of embedded development and AI”. For high-altitude operation scenarios, we developed a welding robot equipped with AI visual positioning, which precisely controls the welding trajectory of the mechanical arm through embedded hardware, and combines AI algorithms to correct deviations in real-time, thus avoiding the risks of manual high-altitude operations while improving welding accuracy. This shift in thinking has transformed embedded development practitioners from being “executors of technology” to “designers of technology and scene integration”, giving us irreplaceable core value in the competitive landscape of the AI era. Professor Wang Yuquan stated, “Deliberate evolution” is not about “blindly chasing trends”, but about “anchoring core directions and continuously iterating and upgrading”. For embedded development practitioners in the field of construction robots, this “core direction” is the “deep integration with AI”: embedded development is the foundation, and AI is the enabling means. The combination of the two can not only help construction robots transition from “automation” to “intelligence”, meeting the real needs of the industry, but also allow us to break through technical limitations and achieve a leap in professional value. The wave of AI is irreversible, and the “deliberate evolution” of the construction robot industry has already begun. As embedded development practitioners, we need not fear technological changes; instead, we should actively embrace this change—using embedded development as our foundation and AI technology as our wings, continuously delving into the path of “hardware and AI integration”, both assisting the construction robot industry in achieving intelligent upgrades and finding our own growth coordinates in this “deliberate evolution”.