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强大、更可靠的重要途径。 该项目将开发用于自主机器人技能学习的算法，新手爱好者可以轻松使用低成本机器人。如果广泛部署，这种方法可以用来收集大量的机器人运动，这些运动可以结合起来提高机器人的技能。 事实证明，从计算机视觉到语音识别，大型数据集的可用性在机器学习应用领域至关重要，而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。深度学习已成为驾驭现实世界复杂性的强大技术。深度学习的成功取决于大型数据集的可用性，而传统上机器人学习很难获得大型数据集。该项目将专注于深度学习算法，可用于有效且可靠的机器人技能学习，直接从原始感官输入生成智能动作，着眼于实现大规模数据收集的广泛部署。为此，拟议的研究旨在：（1）设计可靠且强大的现实世界机器人学习算法，可以在没有人类监督或干预的情况下收集经验； (2) 构建以迁移学习为中心的算法，利用先前任务的经验，可以利用可能不同的机器人平台，大幅加快新技能的学习速度； （3）设计能够有效控制异构、低成本、不精确机器人的算法，以促进广泛部署和项目的教育使命。