Autonomous functions for robots, such as spontaneity, are highly sought after. Many control mechanisms for autonomous robots are inspired by the functions of animals, including humans. Roboticists often design robot behaviors using predefined modules and control methodologies, which makes them task-specific, limiting their flexibility. Researchers offer an alternative machine learning-based method for designing spontaneous behaviors by capitalizing on complex temporal patterns, like neural activities of animal brains. They hope to see their design implemented in robotic platforms to improve their autonomous capabilities.

There is an aspect of high-dimensional chaos called chaotic itinerancy (CI) which can explain brain activity during memory recall and association,

Researchers and roboticists are continually trying to achieve autonomous functions in robots, and they often look toward the animal brain as a point of inspiration for control mechanisms. Because of the task-specific nature of robotic behavior, due to the reliance on predefined modules and control methodologies, they are often limited in flexibility.The newest development in this area is coming out of the University of Tokyo, where researchers have created an alternative machine learning-based method to give robotic AI spontaneous behaviors. The team did this by relying on intricate temporal patterns, such as an animal brain's neural activities.The research was published in Science Advances, titled "Designing spontaneous behavioral switching via chaotic itinerancy." High-Dimensional ChaosA dynamical system is a mathematical model of the ever-changing internal states of something, which describes robots and their control software. Researchers are especially focused on high-dimensional chaos, a class of dynamical systems, due to its impressive ability to model animal brains.

Robots and their control software can be classified as a dynamical system, a mathematical model that describes the ever-changing internal states of something. There is a class of dynamical system called high-dimensional chaos, which has attracted many researchers as it is a powerful way to model animal brains. However, it is generally hard to gain control over high-dimensional chaos owing to the complexity of the system parameters and its sensitivity to varying initial conditions, a phenomenon popularized by the term "butterfly effect." Researchers from the Intelligent Systems and Informatics Laboratory and the Next Generation Artificial Intelligence Research Center at the University of Tokyo explore novel ways for exploiting the dynamics of high-dimensional chaos to implement humanlike cognitive functions. "There is an aspect of high-dimensional chaos called chaotic itinerancy (CI) which can explain brain activity during memory recall and association," said doctoral student Katsuma Inoue.