PDF(4274 KB)
Humanoid Robot Technology and Industry Development
Chenghao Xu, Yaonan Wang, Yang Mo, Qing Zhu
Strategic Study of CAE ›› 2025, Vol. 27 ›› Issue (1) : 150-167.
PDF(4274 KB)
PDF(4274 KB)
Humanoid Robot Technology and Industry Development
With the continuous breakthroughs in technologies such as artificial intelligence, advanced manufacturing, and new materials, China's humanoid robot industry is experiencing explosive growth. Technological innovation and supportive policies have fostered a diversified and competitive landscape. However, China's humanoid robot industry still faces significant challenges, including lagging core technologies, high difficulty in mass production, and obstacles to commercialization. This study explores the major subfields and technological frontiers of humanoid robotics, offering an in-depth analysis of global trends in policies, technologies, and industrial development. It examines the current state of humanoid robotics in China and identifies key opportunities and challenges. Furthermore, the study proposes strategic recommendations to address these challenges, focusing on technological innovation, pilot demonstration, improvement in laws and regulations, and policy support. Specifically, the research recommends encouraging breakthroughs in core technologies, strengthening industrial layout, building humanoid robot infrastructure, and implementing demonstrative projects. These efforts aim to help China's humanoid robot industry overcome technical challenges, improve its industrial ecosystem, and achieve large-scale production and commercialization, thereby enhancing the global competitiveness of the industry.
humanoid robot / core components / large model / embodied intelligence
| [1] |
中华人民共和国工业和信息化部. 关于印发《"十四五"机器人产业发展规划》的通知 [EB/OL]. (2021-12-28)[2024-09-06]. https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2021/art_14c785d5a1124f75900363a0f45d9bbe.html.
Ministry of Industry and Information Technology of the People's Republic of China. Notice on Issuing the "14th Five-Year Plan" for the development of the robotics industry [EB/OL]. (2021-12-28)[2024-09-06]. https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2021/art_14c785d5a1124f75900363a0f45d9bbe.html.
|
| [2] |
新华社. 多地老龄化程度加深 如何面对人口发展新常态? [EB/OL]. (2023-07-04)[2024-09-06]. https://www.gov.cn/yaowen/liebiao/202307/content_6891222.htm.
Xinhua News Agency. The degree of aging is deepening in many places, how to face the new normal of population development? [EB/OL]. (2023-07-04)[2024-09-06]. https://www.gov.cn/yaowen/liebiao/202307/content_6891222.htm.
|
| [3] |
Horizon Europe. Horizon 2020 [EB/OL]. [2024-09-07]. https://commission.europa.eu/funding-tenders/find-funding/eu-funding-programmes/horizon-europe_en.
|
| [4] |
U.S. National Science Foundation. National robotics initiative 3.0 [EB/OL]. [2024-09-07]. https://new.nsf.gov/funding/opportunities/nri-30-national-robotics-initiative-30-innovations-integration/503641/nsf21-559.
|
| [5] |
中华人民共和国工业和信息化部. 工业和信息化部关于印发《人形机器人创新发展指导意见》的通知 [EB/OL]. (2023-11-02)[2024-09-06]. https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2023/art_48fe01d562644aedb7ea3f4256df8190.html.
Ministry of Industry and Information Technology of the People's Republic of China. Ministry of Industry and Information Technology of the People's Republic of China issued the guiding opinions on the innovative development of humanoid robots [EB/OL]. (2023-11-02)[2024-09-06]. https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2023/art_48fe01d562644aedb7ea3f4256df8190.html.
|
| [6] |
Rombach R, Blattmann A, Lorenz D, et al. High-resolution image synthesis with latent diffusion models [R]. New Orleans: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
|
| [7] |
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale [EB/OL]. (2022-06-03)[2024-09-02]. https://arxiv.org/abs/2010.11929.
|
| [8] |
Zhang A, Lipton Z C, Li M, et al. Dive into deep learning [M]. Cambridge: Cambridge University Press, 2023.
|
| [9] |
Han K, Wang Y H, Chen H T, et al. A survey on vision transformer [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(1): 87‒110.
|
| [10] |
Saavedra-Ruiz M, Morin S, Paull L. Monocular robot navigation with self-supervised pretrained vision transformers [R]. Toronto: 2022 19th Conference on Robots and Vision (CRV), 2022.
|
| [11] |
Han Y H, Yu K L, Batra R, et al. Learning generalizable vision-tactile robotic grasping strategy for deformable objects via transformer [J]. IEEE/ASME Transactions on Mechatronics, 2024 (99): 1‒13.
|
| [12] |
Huang W H, Zhou Y X, He X K, et al. Goal-guided transformer-enabled reinforcement learning for efficient autonomous navigation [J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(2): 1832‒1845.
|
| [13] |
Cao H Q, Tan C, Gao Z Y, et al. A survey on generative diffusion models [J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 2814‒2830.
|
| [14] |
Ho J, Jain A, Abbeel P, et al. Denoising diffusion probabilistic models [R]. Vancouver: The 34th International Conference on Neural Information Processing Systems, 2020.
|
| [15] |
Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets [R]. Montreal: The 28th International Conference on Neural Information, 2014.
|
| [16] |
Liu J W, Stamatopoulou M, Kanoulas D. DiPPeR: Diffusion-based 2D path planner applied on legged robots [R]. Yokohama: 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024.
|
| [17] |
Zhao W X, Zhou K, Li J Y, et al. A survey of large language models [EB/OL]. (2023-03-31)[2024-09-20]. https://arxiv.org/abs/2303. 18223.
|
| [18] |
Zhang R, Isola P, Efros A A, et al. The unreasonable effectiveness of deep features as a perceptual metric [R]. Salt Lake City: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
|
| [19] |
Hermann K M, Kočiský T, Grefenstette E, et al. Teaching machines to read and comprehend [R]. Montreal: The 29th International Conference on Neural Information Processing Systems, 2015.
|
| [20] |
Daniel N, Subhro R, Rohan P, et al. Grounding robot plans from natural language instructions with incomplete world knowledge [R]. Zürich: The 2nd Annual Conference on Robot Learning 2018, 2018.
|
| [21] |
Zhang C, Chen J X, Li J T, et al. Large language models for human-robot interaction: A review [J]. Biomimetic Intelligence and Robotics, 2023, 3(4): 100131.
|
| [22] |
Nichols E, Gao L, Gomez R, et al. Collaborative storytelling with large-scale neural language models [R]. Virtual Event: The 13th ACM SIGGRAPH Conference on Motion, Interaction and Games, 2020.
|
| [23] |
Schwitzgebel E, Schwitzgebel D, Strasser A. Creating a large language model of a philosopher [J]. Mind & Language, 2024, 39(2): 237‒259.
|
| [24] |
Li X, Zhong H X, Zhang B, et al. A general Chinese chatbot based on deep learning and its' application for children with ASD [J]. International Journal of Machine Learning and Computing, 2020, 10(4): 519‒526.
|
| [25] |
Satya Durga Manohar Sahu V, Samal P, Kumar Panigrahi C. Modelling, and control techniques of robotic manipulators: A review [J]. Materials Today: Proceedings, 2022, 56: 2758‒2766.
|
| [26] |
Rupert L, Hyatt P, Killpack M D. Comparing model predictive control and input shaping for improved response of low-impedance robots [R]. Seoul: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 2015.
|
| [27] |
Haffemayer A, Jordana A, Fourmy M, et al. Model predictive control under hard collision avoidance constraints for a robotic arm [R]. New York: 2024 21st International Conference on Ubiquitous Robots (UR), 2024.
|
| [28] |
Kim D, Di Carlo J, Katz B, et al. Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control [EB/OL]. (2019-09-14)[2024-09-12]. https://arxiv.org/abs/1909. 06586v1.
|
| [29] |
Li H, Wensing P M. Cafe-mpc: A cascaded-fidelity model predictive control framework with tuning-free whole-body control [EB/OL]. (2024-03-06)[2024-09-15]. https://arxiv.org/abs/2403.03995v3.
|
| [30] |
Singh B, Kumar R, Singh V P. Reinforcement learning in robotic applications: A comprehensive survey [J]. Artificial Intelligence Review, 2022, 55(2): 945‒990.
|
| [31] |
Hua J, Zeng L C, Li G F, et al. Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning [J]. Sensors, 2021, 21(4): 1278.
|
| [32] |
Zhu X J, Zheng X D, Zhang Q Y, et al. Sim-to-real transfer with action mapping and state prediction for robot motion control [R]. Tokyo: 2021 6th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 2021.
|
| [33] |
Portela T, Cramariuc A, Mittal M, et al. Whole-body end-effector pose tracking [EB/OL]. (2024-09-24)[2024-09-29]. https://arxiv.org/abs/2409.16048.
|
| [34] |
Siekmann J, Godse Y, Fern A, et al. Sim-to-real learning of all common bipedal gaits via periodic reward composition [R]. Xi'an: 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021.
|
| [35] |
Qin B Y, Gao Y, Bai Y. Sim-to-real: Six-legged robot control with deep reinforcement learning and curriculum learning [R]. Singapore: 2019 4th International Conference on Robotics and Automation Engineering (ICRAE), 2019.
|
| [36] |
Kobayashi M, Buamanee T, Uranishi Y, et al. ILBiT: Imitation learning for robot using position and torque information based on bilateral control with transformer [EB/OL]. (2024-01-30)[2024-09-20]. https://arxiv.org/abs/2401.16653v2.
|
| [37] |
Stepputtis S, Campbell J, Phielipp M, et al. Language-conditioned imitation learning for robot manipulation tasks [R]. Vancouver: The 34th International Conference on Neural Information Processing Systems, 2020.
|
| [38] |
Wu F Y, Gu Z Y, Wu H R, et al. Infer and adapt: Bipedal locomotion reward learning from demonstrations via inverse reinforcement learning [R]. Yokohama: 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024.
|
| [39] |
Wu Q, Zhang C, Liu Y C. Custom sine waves are enough for imitation learning of bipedal gaits with different styles [R]. Guilin: 2022 IEEE International Conference on Mechatronics and Automation (ICMA), 2022.
|
| [40] |
Zhang W, Yu Z G, Chen X C, et al. The magneto-thermal analysis of a high torque density joint motor for humanoid robots [R]. Beijing: 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), 2018.
|
| [41] |
Urata J, Hirose T, Namiki Y, et al. Thermal control of electrical motors for high-power humanoid robots [R]. Nice: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008.
|
| [42] |
Vassileva D, Kiyosawa Y, Suzuki M. Sensorless torque control for a robot with harmonic drive reducers [J]. Mechanics Based Design of Structures and Machines, 2011, 39(2): 253‒267.
|
| [43] |
Uemura M, Mitabe Y, Kawamura S. Simultaneous gravity and gripping force compensation mechanism for lightweight hand-arm robot with low-reduction reducer [J]. Robotica, 2019, 37(6): 1090‒1103.
|
| [44] |
Ogawa Y, Venture G, Ott C. Dynamic parameters identification of a humanoid robot using joint torque sensors and/or contact forces [R]. Madrid: 2014 IEEE-RAS International Conference on Humanoid Robots, 2014.
|
| [45] |
Almassri A M, Wan Hasan W Z, Ahmad S A, et al. Pressure sensor: State of the art, design, and application for robotic hand [J]. Journal of Sensors, 2015, 2015(1): 846487.
|
| [46] |
Wang Z M, An J, Nie J H, et al. A self-powered angle sensor at nanoradian-resolution for robotic arms and personalized medicare [J]. Advanced Materials, 2020, 32(32): 2001466.
|
| [47] |
Samatas G G, Pachidis T P. Inertial measurement units (IMUs) in mobile robots over the last five years: A review [J]. Designs, 2022, 6(1): 17.
|
| [48] |
International Federation of Robotics. Robotics research: How Asia, Europe and America invest [EB/OL]. [2024-09-20]. https://ifr.org/ifr-press-releases/news/robotics-research-how-asia-europe-and-america-invest.
|
| [49] |
人民网研究院. 人形机器人技术专利分析报告. [R]. 北京: 人民网研究院, 2023.
People's Daily Online Institute. Analysis report on humanoid robot technology patents [R]. Beijing: People's Daily Online Institute, 2023.
|
| [50] |
安徽省人民政府办公厅. 安徽省人民政府办公厅关于印发《安徽省机器人产业发展规划(2018—2027年)》的通知 [EB/OL]. (2018-07-12)[2024-09-06]. https://www.ah.gov.cn/public/1681/8265651.html.
Office of the People's Government of Anhui Province. Notice of the Office of the People's Government of Anhui Province on printing and distributing the development plan for the robot industry in Anhui Province (2018—2027) [EB/OL]. (2018-07-12)[2024-09-06]. https://www.ah.gov.cn/public/1681/8265651.html.
|
| [51] |
广州市工业和信息化局. 关于印发《广州市关于推进新一代人工智能产业发展的行动计划(2020—2022年)》的通知 [EB/OL]. (2020-03-03)[2025-01-17]. https://www.zhinengxiehui.com/ai/24864.html.
Guangzhou Bureau of Industry and Information Technology. Notice on printing and distributing the action plan for promoting the development of the new generation of artificial intelligence industry in Guangzhou (2020—2022) [EB/OL]. (2020-03-03)[2025-01-17]. https://www.zhinengxiehui.com/ai/24864.html.
|
| [52] |
湖北省人民政府. 省人民政府关于印发《湖北省新一代人工智能发展总体规划(2020—2030年)》的通知 [EB/OL]. (2020-09-04)[2024-09-06]. https://www.hubei.gov.cn/zfwj/ezf/202009/t20200904_2887689.shtml.
People's Government of Hubei Province. Notice of the Provincial People's Government on printing and distributing the overall plan for the development of new-generation artificial intelligence in Hubei Province (2020—2030) [EB/OL]. (2020-03-03)[2024-09-06]. https://www.hubei.gov.cn/zfwj/ezf/202009/t20200904_2887689.shtml.
|
| [53] |
中华人民共和国民政部. 关于印发《智慧健康养老产业发展行动计划(2021—2025年)》的通知 [EB/OL]. (2021-10-27)[2024-09-06]. https://www.mca.gov.cn/n152/n165/c39347/content.html.
Ministry of Civil Affairs of the People's Republic of China. Notice on issuing the action plan for the development of the smart health and elderly care industry (2021—2025) [EB/OL]. (2021-10-27)[2024-09-06]. https://www.mca.gov.cn/n152/n165/c39347/content.html.
|
| [54] |
中华人民共和国工业和信息化部. 关于推动轻工业高质量发展的指导意见 [EB/OL]. (2022-06-08)[2024-09-06]. https://www.gov.cn/zhengce/zhengceku/2022-06/19/content_5696665.htm.
Ministry of Industry and Information Technology of the People's Republic of China. Guiding opinions on promoting high-quality development of the light industry [EB/OL]. (2022-06-08)[2024-09-06].https://www.gov.cn/zhengce/zhengceku/2022-06/19/content_5696665.htm.
|
| [55] |
高工咨询. 中国人形机器人产业发展蓝皮书2024 [R].深圳: 高工咨询, 2024.
Gaogong Consulting. Blue book on the development of China's humanoid robot industry 2024 [R]. Shenzhen: Gaogong Consulting, 2024.
|
/
| 〈 |
|
〉 |
AI Summary
