CAREER: Deep Robotic Learning with Large Datasets: Toward Simple and Reliable Lifelong Learning Frameworks

职业：大数据集的深度机器人学习：迈向简单可靠的终身学习框架

• 批准号: 1651843
• 负责人: Sergey Levine
• 金额: $ 55万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651843&HistoricalAwards=false
• 关键词: CAREER; Deep; Robotic; Learning; Large

Learning robot behaviors from large data sets is an important way to make robots more capable and reliable. This project will develop algorithms for autonomous robotic skill learning that can easily be used by novice hobbyists with low-cost robots. If deployed widely, such an approach could be used to gather a large number of robotic motions, which can be combined to improve the robot's skills. Availability of large datasets has proven critical in machine learning application areas, from computer vision to speech recognition, and the ability to collect a large amount of robotic interaction data would substantially increase the capabilities of learning-based robotic systems. Since the approach will be designed for untrained users, it also doubles as an effective tool for robotics education.Deep learning has emerged as a powerful technique for taming the complexity of the real world. The success of deep learning depends on the availability of large datasets, which traditionally have been difficult to obtain for robotic learning. This project will focus on deep learning algorithms that can be used for effective and reliable robotic skill learning, generating intelligent actions directly from raw sensory input, with an eye towards enabling widespread deployment for large-scale data collection. To that end, the proposed research will aim to: (1) devise reliable and robust real-world robotic learning algorithms that can collect experience without human oversight or intervention; (2) build algorithms centered around transfer learning, whereby experience from prior tasks can be used to inform dramatically faster learning of new skills with potentially different robotic platforms; and (3) devise algorithms that can effectively control heterogeneous, low-cost, imprecise robots, so as to facilitate widespread deployment and the project's educational mission.

从大数据集中学习机器人行为是使机器人更有能力和可靠的重要方法。 该项目将开发用于自主机器人技能学习的算法，这些算法可以由具有低成本机器人的新手业余爱好者很容易使用。如果广泛部署，则可以使用这种方法来收集大量的机器人动作，可以将其结合起来以提高机器人的技能。 从计算机视觉到语音识别的机器学习应用领域，大型数据集的可用性已被证明至关重要，并且收集大量机器人交互数据的能力将大大提高基于学习的机器人系统的能力。由于该方法将是为未经培训的用户设计的，因此它也可以作为机器人教育的有效工具的两倍。深入学习已成为驯服现实世界的复杂性的强大技术。深度学习的成功取决于大型数据集的可用性，这在传统上很难获得机器人学习。该项目将集中于深度学习算法，这些算法可用于有效且可靠的机器人技能学习，直接从原始感觉输入中产生智能动作，并着眼于为大规模数据收集提供广泛的部署。为此，拟议的研究的目的是：（1）设计可靠，健壮的现实世界机器人学习算法，这些算法可以在没有人类的监督或干预的情况下收集经验； （2）构建以转移学习为中心的算法，从而可以使用从先前任务中的经验来为具有不同机器人平台的新技能提供明显更快的学习； （3）设计可以有效控制异质，低成本，不精确的机器人的算法，以促进广泛的部署和项目的教育任务。