Robotic Learning with Reusable Datasets

现代机器人学的主要挑战之一是使机器人能够在开放世界环境中执行广泛的任务，如家庭、办公室、医院和建筑工地。这类网站的特点是彼此非常不同，包括提前未知的事件。机器学习已经成为实现这种广泛推广的最有效方法之一，特别是在需要像人类一样感知的领域，如视觉。然而，机器学习在机器人学中的有效应用存在一个主要问题：它们需要收集足够大的观测数据(称为数据集)来实现如此广泛的泛化。虽然广泛重用和共享的数据集在基于计算机的视觉识别等领域实现了广泛的泛化，但这种数据重用在机器人学中是困难的。在这个项目中，研究人员的目标是开发能够利用大量可重复使用的数据集进行机器人学习的方法和技术，以便相同的数据可以在广泛的任务和领域(每种情况下都有一些特定于领域的适量收集)重复使用，同时实现广泛的泛化。重点将放在直接从视觉观察中学习机器人新技能的方法上，使用人类提供的数据和机器人本身收集的数据。研究人员的目标是开发这样的算法，并收集和传播合适的数据集，供其他研究人员重复使用。如果成功，这个项目可能会带来在不同现实世界环境中控制机器人的新方法，以及进一步促进未来机器人学习研究的工具和资源，使可能没有能力自行收集大型数据集的科学家和工程师能够访问机器人学习。启用可重复使用的数据的机器人学习需要解决几个重要问题：人们如何开发可以重复使用这些数据的机器人学习技术？人们如何收集可用于多个机器人、应用程序和环境的数据集？回答这些问题将需要机器人学习的新算法工具，新的数据收集方法，当然还有收集数据集本身。调查人员的技术方法将集中在两个关键问题上。推力1将涉及数据重用的算法开发，其本身将分为两部分：第一部分，重点是模仿学习，第二部分，重点是强化学习。在推力2中，目标是收集大量可重复使用的数据集，这些数据集可以被广泛的机器人研究社区有效利用。《推力2》的第一部分将围绕开发用于大容量数据收集的开源工具展开。第二部分将集中于收集和传播数据集本身。该项目由跨部门的机器人基础研究计划支持，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

One of the major challenges in modern robotics is making robots capable of performing a broad range of tasks in open-world environments, such as homes, offices, hospitals, and construction sites. Such sites are characterized by being very different from each other and include events that are unknown ahead of time. Machine learning has emerged as one of the most effective approaches to allow such broad generalization, particularly in areas that require human like sensing, such as the visual sense. However, effective applications of machine learning to robotics suffer from a major problem: they require collecting large enough observations (referred as to datasets) to enable such broad generalization. While widely reused and shared datasets have enabled broad generalization in areas, such as computer-based vision recognition, this data reuse is difficult in robotics. In this project, the investigators’ goal is to develop methods and techniques that can make it possible to utilize large reusable datasets for robotic learning, such that the same data can be reused repeatedly for a wide range of tasks and domains (with some modest amount of domain-specific collection in each case), while enabling broad generalization. The focus will be on methods for directly learning new skills for robots from the visual observations, using both human-provided data and collected data by the robot itself. The investigators will aim to both develop such algorithms and to collect and disseminate suitable datasets that other researchers can reuse. If successful, this project may lead both to new methods for controlling robots in diverse real-world settings, and tools and resources that can further facilitate future research on robotic learning, making it accessible to scientists and engineers that may not have the capability to collect large datasets on their own.Enabling robotic learning with reusable data requires resolving several important questions: How do people develop robotic learning techniques that can reuse such data? How can people gather the kinds of datasets that can be used for multiple robots, applications, and environments? Answering these questions will require new algorithmic tools for robotic learning, new data collection methodologies, and of course collecting the datasets themselves. The investigators’ technical approach will be focused around two key thrusts. Thrust 1 will be concerned with algorithms development for data reuse, which itself will be divided into two parts: the first part, focuses on imitation learning, and the second part, focuses on reinforcement learning. In Thrust 2, the objective is to collect large and reusable datasets that can be effectively utilized by the broad robotics research community. The first part of Thrust 2 will focus around the development of open-source tools for high-volume data collection. The second part will focus on collecting and disseminating the datasets themselves.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.