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

机器人正在融入生活的更多领域，不再局限于工厂的可预测环境，执行同样的任务。在人类之间工作的机器人需要更高的智商和适应不可预测环境中不断变化的任务的能力。这项工作通过对一种非常聪明、能干和适应能力极强的昆虫--螳螂的大脑控制系统进行建模，为机器人开发了一个复杂的控制系统。这项工作有望改变我们在机器人学和神经科学中对智能的理解。昆虫相对简单的大脑中的决策模型推动了对更复杂大脑的研究。然后，该模型将被用来允许腿部机器人调整其运动，以适应其目标，如帮助人类，或其“需求”，如寻求能量或避免危险。这些进步旨在赋予机器人动物所拥有的自主性。机器人可以接受训练，继续从经验中学习，甚至在新的情况下也能提高效率，而不是针对每一种可能的情况进行编程。在市中心一所学校的课外机器人项目中，学生们将受益于以独特的角度创建机器人的实践经验，这些机器人以生物启发的设计和神经系统建模的独特视角。该工作扩展了合成神经系统(SNS)这一螳螂神经系统的连续时间动力学模型的规模和复杂性，并将其应用于机器人控制。多通道神经记录和刺激技术揭示了昆虫如何通过将计算分散到整个神经系统来简化运动控制。这个项目利用这些技术来理解“较高”级别(处理感官输入的大脑)的能力是如何由“较低”级别(协调腿的神经节)的智力直接支持的。这些数据将被用来开发和实现一个SNS来控制六条腿的MantisBot，赋予它在线学习和智能自主。神经生物学将在所有三个具体目标上为这项工作提供信息：1)调查螳螂神经系统的低级智能，并利用结果增加MantisBot低级控制网络的智商；2)调查下行命令和行为之间的相关性，并利用结果为MantisBot开发一个简化的大脑(即高级控制器)；以及3)调查相互冲突的视觉输入(例如，同时捕食和捕食)对下行命令的影响，并利用这些发现赋予MantisBot分布在整个SNS中的强大智能。

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.