IRFP: Deep Neural Networks for Perception and Action Integration in Robotic Control

国际研究奖学金项目使美国科学家和工程师能够在国外进行9至24个月的研究。该计划的奖励为联合研究提供了机会，并利用独特或互补的设施、专业知识和国外的实验条件。该奖项将支持Alan Lockett博士与瑞士卢加诺人工智能研究所（IDSIA）的Juergen Schmidhuber教授进行为期24个月的研究。该项目探索了训练深度神经网络来控制具有人形手和手臂的物理机器人的方法。最近，深度人工神经网络训练新方法的发展使得人工智能领域的许多基准都取得了突破。深度神经网络目前保持着基准预测任务的记录，包括手写识别（MNIST）和对象识别（NORB, CIFAR-10）。该项目正在开发使用超过4,096个处理器的大规模多处理器gpu阵列训练具有数千或数百万个参数的深度神经网络控制器的方法。本研究中的深度网络控制器使用神经进化、强化学习以及两者的结合进行训练。这种控制器被用来训练一个人形iCub机器人来操纵物体，参加AAAI小规模操纵挑战赛。这项研究是在瑞士卢加诺的人工智能研究所（IDSIA）与Juergen Schmidhuber教授合作进行的。IDSIA是研究深度神经网络、人工进化、强化学习和机器人控制的领先研究机构。这项研究旨在提高我们对机器人控制的总体理解。将层次感知和动作模块集成在一起的深度神经网络有可能在复杂机器人系统的控制方面取得突破，从而使计算机和机器人系统的部署具有比目前更大的自主性。下个世纪机器人技术的部署很可能反映了上个世纪计算机技术的快速引入。正如本研究所研究的，这些机器人成功的背后将是深度分层控制系统，该系统将感知和行动集成在高抽象水平上。这项研究考察了那些有潜力以无数方式改变和改善我们生活的技术。在未来，自动驾驶汽车将相互协调，并与一个主动的道路，以尽量减少事故和提高效率。先进的自动驾驶技术将最终使个人飞行器成为现实。自主机器人矿工将降低人类的风险，同时改善对原材料和资源的获取。机器人外科医生将以更高的精度执行复杂的手术。这些技术中的每一种都严重依赖于感知分析和分层控制器的深度集成。研究中所研究的深度神经网络的使用构成了一种很有前途的方法，可以将这些新技术从实验室带到我们的日常生活中。

The International Research Fellowship Program enables U.S. scientists and engineers to conduct nine to twenty-four months of research abroad. The program's awards provide opportunities for joint research, and the use of unique or complementary facilities, expertise and experimental conditions abroad. This award will support a twenty-four-month research fellowship by Dr. Alan Lockett to work with Professor Juergen Schmidhuber at the Instituto dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA) in Lugano, Switzerland. This project explores methods for training deep neural networks to control a physical robot with humanoid hands and arms. The recent development of new methods for training deep artificial neural networks has resulted in breakthroughs on a number of benchmarks in artificial intelligence. Deep neural networks currently hold the record for benchmark predictive tasks including handwriting recognition (MNIST) and object recognition (NORB, CIFAR-10). This project is developing methods for training deep neural network controllers with thousands or millions of parameters using an array of massively multiprocessor GPUs with over 4,096 processors. Deep network controllers in this research are trained using neuroevolution, reinforcement learning, and combinations of the two. Such controllers are being used to train a humanoid iCub robot to manipulate objects for the AAAI Small-Scale Manipulation Challenge.The research is being performed at the Instituto dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA) in Lugano, Switzerland in conjunction with Professor Juergen Schmidhuber. IDSIA is a leading research institution in the study of deep neural networks, artificial evolution, reinforcement learning, and robotic control. This research seeks to advance our understanding of robotic control in general. The focus on deep neural networks that integrate hierarchical perception and action modules has the potential to result in breakthroughs in control of complex robotic systems that would enable the deployment of computer and robotic systems that operate with greater autonomy than is currently possible.The deployment of robotic technologies over the course of the next century is likely to mirror the rapid introduction of computer technology in the past century. Behind the success of these robots will be deep hierarchical control systems that integrate perception and action at a high level of abstraction, as studied in this research. This research examines technologies that hold the potential to transform and improve our lives in innumerable ways. In the future, self-driving cars will co-ordinate with each other and with an active roadway to minimize accidents and improve efficiency. Advanced autopilot technology will finally make personal flying vehicles a reality. Autonomous robotic miners will reduce risk to humans while improving access to raw materials and resources. Robotic surgeons will perform complex operations with new levels of precision. Each of these technologies depends critically on the availability of deep integration of perceptual analysis and hierarchical controllers. The use of deep neural networks like the ones studied in the proposed research constitutes a promising approach to bringing these new technologies out of the lab and into our daily lives.