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使机器人更有能力和可靠的重要方法。 该项目将开发自主机器人技能学习的算法，这些算法可以很容易地被低成本机器人的新手爱好者使用。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。 大型数据集的可用性已被证明在机器学习应用领域至关重要，从计算机视觉到语音识别，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，它也是机器人教育的有效工具。深度学习已经成为驯服真实的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，而这些数据集在传统上很难用于机器人学习。该项目将专注于深度学习算法，可用于有效和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，可以在没有人为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，从而可以使用先前任务的经验来通知使用可能不同的机器人平台快速学习新技能。以及（3）设计算法，可以有效地控制异构，低成本，不精确的机器人，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.