Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Traditional joint-link robots have been widely used in production lines because of their high precision for single tasks. With the development of the manufacturing and service industries, the requirement for the comprehensive performance of robotics is growing. Numerous types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation. A study route from inner mechanisms to external structures is proposed to imitate humans and animals better. With this idea, a brain-inspired intelligent robotic system is constructed that contains visual cognition, decision-making, motion control, and musculoskeletal structures. The research team of Prof. Qiao Hong (Institute of Automation, Chinese Academy of Sciences) reviews cutting-edge research in brain-inspired visual cognition, decision-making, motion control, and musculoskeletal systems. Two software systems and a corresponding hardware system are established, aiming at the verification and applications of next-generation brain-inspired musculoskeletal robots. The review is published in the first issue of Machine Intelligence Research in 2023. Full text is open accessed!



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Robot was acclaimed as “the jewel on the crown of the manufacturing industry”. It has become a critical indicator for measuring the comprehensive strength of a country in science and technology. Since the first industrial robot, Unimate, went online in 1962, plenty of robotic systems have been employed in industrial production lines, replacing workers to complete tasks with high repetition, high risk, and heavy load, continuously creating tremendous production benefits for manufacturing enterprises. With continuous advancements in mechanical engineering, control, and artificial intelligence technologies, an increasing number of applications, including national defense, health care, and advanced manufacturing, have put forward urgent demands for robotic applications. However, the performance of existing robotic systems is still limited by sensor precision, repetition accuracy of the systems, and the performance of auxiliary mechanisms. Advancing the performance of robots requires the synergetic development of multiple disciplines, such as mechanical engineering, material science, control theory, intelligent chips, and information science, which significantly limits the broad application of robots owing to the requirement of high investment and a long development cycle.


In comparison, humans can achieve an outstanding overall performance superior to the independent capability of each body unit under the condition of low precision of sensory feedback, behavior control, and limited computation dissipation. It mainly benefits from the extraordinary ability of humans to integrate information from multiple brain regions and sufficiently exploit the advantages of the body structure. Therefore, research on human-inspired intelligent robots by mimicking their biological structure, behavioral features, intelligent principles, and control mechanisms will be significant in developing new-generation robots.


Humans have long been considered the reference standard for improving robots. At present, according to the difference in motivation, the research work of human-inspired robots mainly includes two routes. The first is to mimic the functionality of humans. Based on the observation and analysis of human behavior, this research route aims to endow robots with the ability to complete human work. For example, the most widely used articulated robots are composed of rigid links, gearboxes, joint reducers, and auxiliary tools. Although they are very different from humans in appearance and actuating principles, they can replace humans in completing specific operation tasks with the help of sophisticated sensors and specialized control programs.



System framework of the simulation platform


Another typical example is a humanoid robot with a human-like appearance and function designed to mimic human mechanisms from outside-in. Representative studies include advanced step innovative mobility (ASIMO) (1997C) by Honda Motor, BHR (2000C) by the Beijing Institute of Technology, iCub (2008C) by the Italian Institute of Technology, and Atlas (2016C) by Boston Dynamics. These robots are normally endowed with bipedal locomotion and have a preliminary self-learning ability and adaptability to complex tasks by introducing an artificial intelligence algorithm.


By deploying highly redundant actuators, humanoid robots are capable of human-like dexterity; however, challenges for the design of controllers are introduced simultaneously. Many mathematical algorithms have been proposed to control humanoid robots with highly redundant actuators, such as the zero-moment point, capture point, and central pattern generator algorithms, for stable bipedal locomotion, model free reinforcement learning, and imitation learning based on demonstration for diverse manipulation. Although these methods are still in the early stages of research, they exhibit remarkable performance and might be a promising avenue for promoting the application range of humanoid robots.


The second route is oriented by mimicking the core and fundamental mechanism of humans, forming the research of brain-inspired intelligent robots. Brain-inspired intelligent robots imitate humans, from their inner mechanisms to their external structures, by referring to the neural mechanisms and structural characteristics of visual cognition, decision-making, motion control, and musculoskeletal systems.


By integrating multiple disciplines of brain science, artificial intelligence, and robotics, research on brain-inspired intelligent robots aims to build brain-inspired information processing algorithms with high computational efficiency and general intelligence and musculoskeletal systems with high flexibility and strong robustness, laying the foundation for next-generation robot development. In the past few years, because of the nature of exploring humans and the urgent need for the treatment of mental diseases, many countries, including the United States, the European Union, Japan, Republic of Korea, Canada, Australia, and China, have proposed brain projects to support research in brain science and brain-inspired intelligence.


As an indispensable part of brain-inspired intelligence, brain-inspired intelligent robots act as comprehensive systems integrated with software and hardware, which are of great significance in verifying the research results. Many research groups have conducted preliminary research and have achieved many milestones in brain-inspired algorithms, neuromorphic chips, and musculoskeletal robotic systems. As a typical application form of the musculoskeletal system, wearable exoskeleton system has been a hot spot in recent years.



Control framework of the software system



Diagram of the structural design and muscle distribution of the hardware platform


Some promising techniques and frameworks, such as region-based control and interactive framework, human-in-the-loop control, and barrier-energy function design, have been employed to improve the performance of wearable exoskeleton system in assisted walking and weight-bearing work. These works are significant in musculoskeletal applications, and represent the state-of-the-art research in human-machine interaction using bio-inspired wearable exoskeleton systems. Owing to the introduction of human internal mechanisms, brain inspired intelligent robots are expected to achieve empathy with humans and realize deeper cooperation with co-workers.


Compared with previous relevant reviews of brain-inspired robotics, this study introduces the cutting edge works along the research chain of brain-inspired robots, which are composed of instructive neural mechanisms, designing of the brain-inspired algorithm, building the software platform, and development of hardware system. In particular, the latest progress in the system integration of software and hardware is introduced.


The remainder of this paper is organized as follows. Section 2 introduces the core neural mechanisms in vision, decision-making, control, and body structure and the corresponding brain-inspired algorithm. Section 3 introduces the software and hardware system integration. The simulation platform for brain-inspired robots integrates brain-inspired algorithms in vision, decision making, and movement control, providing efficient tools for researchers from different fields. The hardware platform was designed to mimic the human musculoskeletal system, providing a physical system to validate the performance of the brain-inspired algorithm. Section 4 summarizes and concludes the study.


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Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Hong Qiao, Ya-Xiong Wu, Shan-Lin Zhong, Pei-Jie Yin & Jia-Hao Chen

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Release Date: 2023-02-01 Visited: