不止具身智能？傅利叶称1-2年内将脑机接口引入机器人康复训练

Core Insights - The integration of brain-machine interface (BMI) technology with rehabilitation robots is set to achieve large-scale implementation within 1 to 2 years, as announced by Fourier Intelligence's founder Gu Jie at the second Fourier Embodied Intelligence Ecological Summit [1] - The advancements in hardware and software, particularly the emergence of ultrasound BMIs and large model algorithms, are pivotal in overcoming previous limitations in BMI applications [2][3] Group 1: Technological Advancements - The rapid maturation and diversification of hardware, including portable and miniaturized devices, have facilitated the large-scale production of BMIs [2] - The transition from traditional EEG methods to advanced technologies like near-infrared spectroscopy (NIRS) and ultrasound represents a significant evolution in BMI capabilities [2] - The use of large models for processing EEG signals allows for more effective classification and decoding of complex brain signals, enhancing intention recognition [3] Group 2: Market Potential and Applications - The introduction of the "Brain-Machine Embodied Intelligence Rehabilitation Port" aims to enhance rehabilitation efficiency and effectiveness by creating a closed-loop system based on brain intentions [4] - The market for embodied intelligence in rehabilitation is expected to evolve from hospitals to nursing homes and eventually into households, potentially reaching a trillion-dollar market in the next 5 to 10 years [4] - The development of high-quality data sets is crucial for the future capabilities of robots, emphasizing the importance of diverse and relevant data for training embodied intelligence models [5] Group 3: Industry Challenges - Despite the promising advancements, the industry currently lacks large-scale data sets and the necessary soft and hardware infrastructure for widespread adoption [4] - The quality of data is more critical than quantity, with a focus on diverse task execution and successful interaction data being essential for effective learning [5] - The integration of various data sources, including online videos and first-person interaction data, is necessary to create comprehensive training datasets for robots [5]