Significant progress has been made by robotic systems in fields like mechanical engineering, control, and artificial intelligence technologies. However, the performance of present robotic systems still has limitations and fails to fulfill the needs of an increasing number of applications. To tackle these issues, a brain-inspired intelligent robotic system has been constructed.
A research group headed by Professor Qiao Hong from the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, has reviewed the cutting-edge works together with the research chain of brain-inspired robots.
Initially, they introduce the core neural mechanisms in decision-making, vision, control, and body structure and the matching brain-inspired algorithm. Secondly, they present the hardware and software system integration.
The simulation platform available for brain-inspired robots combines brain-inspired algorithms in vision, decision-making, and movement control, thereby offering effective tools for scientists from various fields.
The hardware platform was developed to mimic the human musculoskeletal system, offering a physical system to confirm the performance of the brain-inspired algorithm.
“Brain-inspired motion-learning algorithms can use sparse rewards to realize generalized control policy learning. With this method, robotics can accomplish a series of manipulations after simple training. System robustness comes from redundancy and anti-interference can improve system reliability.”
The researchers describe the benefits of brain-inspired intelligent robotics. Furthermore, they make opinions regarding the future development of next-generation robotics.
“Next-generation robotics could be developed with numerous brain-inspired algorithms and novel musculoskeletal structures. Organic structural design and hardware construction should be reinforced and emphasized. We hope that this generation of robotics can provide inspiration and reference for brain-computer interface control.”
For the development of brain-inspired intelligent robotics, more time and effort is required.
Qiao, H., et al. (2023) Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1390-8.