Robotic Learning with Reusable Datasets

现代机器人技术面临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，例如家庭、办公室、医院和建筑工地。这些地点的特点是彼此之间差异很大，包括事先未知的事件。机器学习已经成为允许这种广泛推广的最有效方法之一，特别是在需要人类感知的领域，如视觉。然而，机器学习在机器人技术中的有效应用面临一个主要问题：它们需要收集足够大的观测值（称为数据集），以实现这种广泛的泛化。虽然广泛重复使用和共享的数据集已经在计算机视觉识别等领域实现了广泛的推广，但这种数据重复使用在机器人技术中是困难的。在这个项目中，研究人员的目标是开发能够利用大型可重用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重复用于各种任务和领域（在每种情况下都有一定数量的特定领域收集），同时实现广泛的泛化。重点将是从视觉观察中直接学习机器人新技能的方法，使用人类提供的数据和机器人本身收集的数据。研究人员将致力于开发此类算法，并收集和传播其他研究人员可以重复使用的合适数据集。如果成功的话，该项目可能会带来在不同的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，使科学家和工程师能够自行收集大型数据集。使用可重用数据实现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？解决这些问题将需要新的机器人学习算法工具，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方面。重点1将关注数据重用的算法开发，其本身将分为两个部分：第一部分侧重于模仿学习，第二部分侧重于强化学习。在Thrust 2中，目标是收集大型和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地利用。Thrust 2的第一部分将专注于开发用于大量数据收集的开源工具。第二部分将集中在收集和传播数据集本身。这个项目是由跨董事会基础研究机器人计划，由工程局（ENG）和计算机与信息科学与工程局（CISE）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价值和更广泛的评估被认为值得支持。影响审查标准。

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.