Editorial: Robotic intelligence and automation

Hong Qiao (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China and CAS Centre for Excellence in Brain Science and Intelligence Technology, Shanghai, China)

Robotic Intelligence and Automation

ISSN: 2754-6969

Article publication date: 28 March 2023

Issue publication date: 28 March 2023



Qiao, H. (2023), "Editorial: Robotic intelligence and automation", Robotic Intelligence and Automation, Vol. 43 No. 1, pp. 1-2. https://doi.org/10.1108/RIA-02-2023-269



Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

Under the joint efforts of the publisher and the editorial team, journal Assembly Automation has been renamed as Robotic Intelligence and Automation (RIA). RIA focuses on the interdisciplinary integration of robotics, artificial intelligence and automation and hardware and software.

Robots have grown tremendously in recent decades and are playing an increasingly important role in the industrial and service sectors. In anticipation of robots replacing humans in more general scenarios, and even becoming human friends, scientists have been conducting ongoing research to give robots human-like structure and intelligence. In 2000, ASIMO, a humanoid robot developed by Honda, received widespread attention for being able to perform actions such as walking, running and grasping both hard and soft objects in response to human commands (Sakagami et al., 2002). In 2005, Boston Dynamics developed a four-legged robot, Big Dog (Raibert et al., 2008), capable of traversing various outdoor terrains and recovering from disturbances. In 2013, Boston Dynamics unveiled its humanoid robot, Atlas (Kuindersma et al., 2016). After several iterations, Atlas's advanced control system and hardware enabled highly complex and agile locomotion, such as running, dancing and backflipping. From 2012 to 2015, the Defense Advanced Research Projects Agency hosted the DARPA Robotics Challenge to promote technological innovation in semiautonomous robots for dangerous tasks such as disaster rescue in complex environments (Guizzo and Ackerman, 2015). HUBO, a robot from the Korean Institute for Science and Technology, won the competition (Oh et al., 2017). In 2020, the University of Liverpool developed a mobile robotic chemist that can move freely around the lab and perform chemical experiments independently with its arms (Burger et al., 2020). In 2022, a robot codenamed Optimus was unveiled by Tesla. The developers of Optimus Prime hope to mass-produce the robot in the future and use it to perform “dangerous, repetitive and boring” tasks, such as assisting in manufacturing[1]. In addition, to better imitate human structure, many musculoskeletal robots with human-inspired joints, muscles and actuation mechanisms have been designed and established by the University of Tokyo (Asano et al., 2017) and Institute of Automation, Chinese Academy of Sciences (Qiao et al., 2023).

However, compared with humans, the intelligence of existing robots is still limited especially in complex and unstructured environments, which restricts the ability of robots to interact naturally with humans, to make flexible decisions in complex unstructured environments and to achieve precise and dexterous manipulation.

Therefore, it is crucial to further enhance the robotic intelligence. In recent years, artificial intelligence, including large language model, has developed rapidly and become deeply integrated into various application scenarios. In 2012, AlexNet (Krizhevsky et al., 2017) significantly improved the performance of image recognition on ImageNet. In 2016, AlphaGo (Silver et al., 2016) beat the world champion in the game Go. The victory is an important milestone for the field of artificial intelligence, proving that machine learning algorithms can master complex strategy games previously thought too difficult for computers. In 2021, AlphaFold (Jumper et al., 2021) was developed by DeepMind as a protein structure prediction system and has achieved state-of-the-art performance in the CASP13 protein structure prediction challenge. From 2018, OpenAI continuously developed large language models GPT-1 (Radford et al., 2018), GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020), which received widespread attention for their impressive performance on a wide range of NLP tasks. GPT can produce highly coherent and reasonable text in a variety of styles and formats. In 2022, OpenAI released their novel chatbot ChatGPT as a web application, which has the fastest user growth rate of any APP in history. ChatGPT has the potential to revolutionize many industries with its powerful language understanding and generation capabilities. Bill Gates said that ChatGPT is as important as PC and internet, and it will change the world.

Although AI research has developed rapidly, it is more concerned with the performance of algorithms and software systems and still has limitations in improving the intelligence of robots and hardware systems. Therefore, the improvement of robot intelligence still requires the intersection of several disciplines such as artificial intelligence, neuroscience, control science and mathematics. Based on the judgment that robotic intelligence will be an important and promising direction in the information field, we decided to rename the journal to RIA to encourage and motivate the development of robotic intelligence.

RIA is a relatively new journal and focuses on theory and application of robotic intelligence and automation from multiple disciplines like artificial intelligence, control science, mechanical engineering, mathematics, neuroscience and material. It welcomes theories of robotic intelligence and automation on perception, cognition, decision-making, control, structure design and their applications to industry, service, surgery, agriculture, marine, space and other fields. Furthermore, integrated intelligence and biologically inspired intelligence of robots are especially encouraged. Welcome to submit your research.

The editorial team of RIA, including the editor-in-chief and associate editors, consists of 15 internationally renowned scholars from five countries in the fields of robotics, artificial intelligence, control, etc. It is a united and upward editorial team, and we hope to work together to make the journal better. More fresh blood and valuable suggestions for the journal are welcomed by the editorial team.



Asano, Y., Okada, K. and Inaba, M. (2017), “Design principles of a human mimetic humanoid: humanoid platform to study human intelligence and internal body system”, Science Robotics, Vol. 2 No. 13.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. and Amodei, D. (2020), “Language models are few-shot learners”, Advances in Neural Information Processing Systems, Vol. 33, pp. 1877-1901.

Burger, B., Maffettone, P., Gusev, V., Aitchison, C., Bai, Y., Wang, X., Li, X., Alston, B.M., Li, B., Clowes, R. and Cooper, A. (2020), “A mobile robotic chemist”, Nature, Vol. 583 No. 7815, pp. 237-241.

Guizzo, E. and Ackerman, E. (2015), “The hard lessons of DARPA's robotics challenge”, IEEE Spectrum, Vol. 52 No. 8, pp. 11-13.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A. and Hassabis, D. (2021), “Highly accurate protein structure prediction with AlphaFold”, Nature, Vol. 596 No. 7873, pp. 583-589.

Krizhevsky, A., Sutskever, I. and Hinton, G. (2017), “ImageNet classification with deep convolutional neural networks”, Communications of the ACM, Vol. 60 No. 6, pp. 84-90.

Kuindersma, S., Deits, R., Fallon, M., Valenzuela, A., Dai, H., Permenter, F., Koolen, T., Marion, P. and Tedrake, R. (2016), “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot”, Autonomous Robots, Vol. 40 No. 3, pp. 429-455.

Oh, P., Sohn, K., Jang, G., Jun, Y. and Cho, B.K. (2017), “Technical overview of team DRC‐Hubo@ UNLV's approach to the 2015 DARPA Robotics Challenge finals”, Journal of Field Robotics, Vol. 34 No. 5, pp. 874-896.

Qiao, H., Wu, Y., Zhong, S., Yin, P. and Chen, J. (2023), “Brain-inspired intelligent robotics: theoretical analysis and systematic application”, Machine Intelligence Research, Vol. 20 No. 1, pp. 1-18.

Radford, A., Narasimhan, K., Salimans, T. and Sutskever, I. (2018), “Improving language understanding by generative pre-training”.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I. (2019), “Language models are unsupervised multitask learners”, OpenAI Blog, Vol. 1 No. 8, p. 9.

Raibert, M., Blankespoor, K., Nelson, G. and Playter, R. (2008), “BigDog, the rough-terrain quadruped robot”, IFAC Proceedings Volumes, Vol. 41 No. 2, pp. 10822-10825.

Sakagami, Y., Watanabe, R., Aoyama, C., Matsunaga, S., Higaki, N. and Fujimura, K. (2002), “The intelligent ASIMO: system overview and integration”, IEEE/RSJ international conference on intelligent robots and systems, Vol. 3, pp. 2478-2483.

Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M. and Hassabis, D. (2016), “Mastering the game of go with deep neural networks and tree search”, Nature, Vol. 529 No. 7587, pp. 484-489.

Related articles