RI: Medium: Collaborative Research: A Structure-Math-Function Approach for Designing Robustly Intelligent Synthetic Nervous Systems

Case Western Reserve University, Cleveland OH

Investigators

Abstract

Robots are becoming integrated into more areas of life, no longer confined to the predictable environment of a factory, performing the same task. Robots that work among humans require greater intelligence and the ability to adapt to changing tasks in an unpredictable environment. This work develops a sophisticated control system for robotics by modeling the control systems in the brain of a remarkably intelligent, capable, and adaptable insect: the praying mantis. This work promises to transform our understanding of intelligence in both robotics and neuroscience. A model of decision-making in the relatively simple brains of insects advances the study of more complex brains. The model will then be used to allow a legged robot to adapt its movement to suit its goals such as assisting humans, or its "needs" such as seeking energy or avoiding danger. These advances seek to give robots the autonomy that animals have. Instead of being programmed for every possible situation, a robot could be trained, continue to learn from experience, and improve efficiency even in novel situations. At an after-school robotics program at an inner-city school, students will benefit from hands-on experience creating robots with the unique perspective of bio-inspired design and modeling of nervous systems. This work expands the scale and sophistication of a synthetic nervous system (SNS), a continuous time dynamical model of praying mantis nervous system, and applies it to robotic control. Multi-channel neural recording and stimulation techniques are revealing how insects simplify motor control by distributing computation throughout the nervous system. This project leverages these techniques to understand how the capability of the "higher" level (the brain, where sensory input is processed) is directly supported by intelligence in the "lower" level (ganglia that coordinate the legs). These data will be used to develop and implement an SNS to control the six-legged MantisBot, endowing it with online learning and intelligent autonomy. Neurobiology will inform this work in all three specific aims: 1) Investigate the lower-level intelligence of the mantis nervous system and use the results to increase the intelligence of MantisBot's low-level control networks; 2) Investigate the correlation between descending commands and behavior and use the results to develop a simplified brain (i.e. high-level controller) for MantisBot; and 3) Investigate the effect of conflicting visual inputs (e.g. simultaneous prey and predator) on descending commands, and use these findings to endow MantisBot with robust intelligence distributed throughout its SNS.