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

从大数据集中学习机器人行为是提高机器人能力和可靠性的重要途径。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的机器人。如果得到广泛应用，这种方法可以用来收集大量的机器人动作，这些动作可以结合起来提高机器人的技能。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已被证明是至关重要的，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。由于这种方法是为未经训练的用户设计的，它也可以作为机器人教育的有效工具。深度学习已经成为一种强大的技术，可以驯服现实世界的复杂性。深度学习的成功取决于大数据集的可用性，而传统上机器人学习很难获得大数据集。该项目将专注于深度学习算法，该算法可用于有效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，并着眼于大规模数据收集的广泛部署。为此，拟议的研究将旨在：(1)设计可靠和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，可以在潜在的不同机器人平台上显著加快新技能的学习；(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。

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.