Why Humanoid Robots Are Booming Yet Hard for the Average Person to Enter – A Quick Look at the Robot Industry Chain

Robot origins and early concepts – The text notes that mechanical humanoids date back to the Western Zhou period in China and to Karel Čapek’s 1920 play that coined the word "robot" (Robota). It also references the 1950 Asimov "Three Laws of Robotics" as a foundational safety consensus.

Generational development – First‑generation robots (e.g., 1947 Oak Ridge remote‑controlled unit, 1962 PUMA teach‑repeat robot) lacked perception. The second generation introduced sensing (force, touch, vision). The third generation added intelligence, enabling autonomous decision‑making.

Industry chain analysis

Upstream – core hardware and software

High‑precision screws, frame‑type and RV reducers, and high‑torque frameless motors (cost 45‑60% of the system).

Key suppliers: 绿的谐波, 中大力德, 汇川技术, 鸣志电器.

Encoders (5‑8% cost) from 汇川技术, 海德汉, 禹衡光学.

AI algorithms (large models, reinforcement learning) and operating systems (ROS, RT‑Thread).

Midstream – robot body manufacturing

Design of mechanical structure, joint layout, and material selection for strength‑to‑weight balance.

Integration of motors, reducers, and encoders into assemblies.

Software embedding (control algorithms, perception models) and multimodal fusion (vision, audio, tactile).

Testing for performance, reliability (extreme temperature, long‑duration), and quality control.

Downstream – application scenarios

Industrial manufacturing (flexible production, hazardous environments).

Commercial services (logistics, retail, elderly care).

Home services (cleaning, entertainment).

Special operations (firefighting, inspection, exploration).

Business models – The article lists four main models: (1) full‑machine sales with after‑sale service fees; (2) RaaS (Robot‑as‑a‑Service) leasing charged by usage; (3) customized solutions combining hardware, software, and scenario adaptation; (4) value‑added subscription services for OTA updates, algorithm upgrades, and data analytics.

Challenges and opportunities – Rapid development creates cost pressures, component bottlenecks (e.g., reducers accounting for ~25% of cost), and safety concerns. AI offers both threats (job displacement) and opportunities (new AI‑driven robot functions, autonomous navigation, predictive maintenance). The text also reflects on broader societal impacts, comparing the current AI‑driven unemployment wave to the 2008 financial crisis.

Strategic recommendations – For firms and individuals, the article suggests: (1) focus on high‑value AI applications (e.g., medical assistance, logistics); (2) adopt modular, upgradable designs; (3) leverage subscription models for recurring revenue; (4) invest in talent skilled in AI, robotics, and data analysis; and (5) continuously monitor policy and market trends such as China’s "15‑5" plan.