An Animal-in-the-Loop, Approximate Dynamic Programming Based Robotic Design Paradigm

基于动物循环、近似动态规划的机器人设计范式

• 批准号: 0233529
• 负责人: Jennie Si
• 金额: $ 39万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0233529&HistoricalAwards=false
• 关键词: Animal; Loop; Approximate; Dynamic; Programming

The future development of space solar power systems presents immense challenges for designers of sophisticated and adaptable robotic systems capable of complex mission assignments in the difficult environment of space. In an effort to help address a subset of these problems, this project will conduct an interdisciplinary investigation integrating the diverse ranges of computer science, neurophysiology, psychology and bioengineering. It will develop a novel animal-in-the-loop robotic architecture for the efficient investigation and optimization of learning algorithms applicable to general cooperative robotic systems. The plan is to eclipse the current trend for bio-inspired robotics through the use of an actual bio-based robot. This will be accomplished by training rats to pilot mobile robots. Rats will be used in place of the traditional sensing mechanisms of robotic systems. In so doing, the researchers aim to exploit the proven superiority of embedded processing paradigms provided by real neural networks. To better understand exactly how and why mammals are such good processors of sensory input and how we might apply their strategies to robotic systems, the researchers will equip each rat with multi-electrode arrays in the sensory regions of their brain. They will collect neural spike data representing the sensory environment as the rat-robot systems complete a cooperative task. Task data and neural recordings will be used in the development and simulation of advanced learning algorithms based on approximate dynamic programming (ADP). A better understanding of sensory processing coupled with advances in ADP models will be used to design more intelligent platforms, namely animal-computer co-adaptation, in cooperative robotic systems. For the development of space solar power objectives, such robotic systems must provide the essential traits of robustness and scalability. The new generalized approach using bio-sensor based, ADP optimized solutins provides a unique high-potential to develop such powerful systems.

空间太阳能发电系统的未来发展对能够在困难的空间环境中执行复杂使命任务的先进和适应性强的机器人系统的设计者提出了巨大的挑战。为了帮助解决这些问题的一个子集，该项目将进行跨学科的调查，整合计算机科学，神经生理学，心理学和生物工程的不同范围。它将开发一种新的动物在环机器人架构的有效调查和优化学习算法适用于一般的合作机器人系统。该计划是通过使用实际的生物机器人来掩盖当前生物启发机器人的趋势。这将通过训练老鼠驾驶移动的机器人来实现。 老鼠将被用来代替机器人系统的传统传感机制。在这样做的过程中，研究人员的目标是利用由真实的神经网络提供的嵌入式处理范例的已被证明的优越性。为了更好地理解哺乳动物是如何以及为什么能够如此好地处理感觉输入，以及我们如何将它们的策略应用于机器人系统，研究人员将在每只大鼠的大脑感觉区域配备多电极阵列。当老鼠-机器人系统完成合作任务时，他们将收集代表感觉环境的神经尖峰数据。任务数据和神经记录将用于基于近似动态规划（ADP）的高级学习算法的开发和模拟。更好地理解感觉处理加上ADP模型的进步将用于设计更智能的平台，即动物-计算机协同适应，在合作机器人系统。为了开发空间太阳能发电目标，这种机器人系统必须具备坚固耐用和可扩展性的基本特征。使用基于生物传感器的ADP优化解决方案的新的通用方法为开发这种强大的系统提供了独特的高潜力。