A hybrid and scalable brain-inspired robotic platform

作者：Zhe Zou, Rong Zhao, Yujie Wu, Zheyu Yang, Lei Tian, Shuang Wu, Guanrui Wang, Yongchao Yu, Qi Zhao, Mingwang Chen, Jing Pei, Feng Chen, Youhui Zhang, Sen Song, Mingguo Zhao, Luping Shi · 发表于：Scientific Reports · 年份：2020 · DOI：10.1038/s41598-020-73366-9 · 被引用次数：23 · 研究领域：Advanced Memory and Neural Computing、Neuroscience and Neural Engineering、Photoreceptor and optogenetics research

Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots' intelligence.