Robotic Learning with Reusable Datasets

使用可重复使用的数据集进行机器人学习

• 批准号: 2150826
• 负责人: Sergey Levine
• 金额: $ 50万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150826&HistoricalAwards=false
• 关键词: Robotic; Learning; Reusable; Datasets

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.

现代机器人技术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，这些场所的特点是彼此非常不同，包括。机器学习已经成为实现这种广泛概括的最有效方法之一，特别是在需要类似人类感知的领域，例如视觉。然而，机器学习在机器人技术中的有效应用却受到了影响。来自一个主要问题：他们需要收集足够大的观察结果（称为数据集）以实现如此广泛的泛化虽然广泛重用和共享的数据集已经在基于计算机的视觉识别等领域实现了广泛的泛化，但在该项目中，这种数据重用是困难的，研究人员的目标是。开发可以利用大型可重用数据集进行机器人学习的方法和技术，以便可以在广泛的任务和领域中重复重用相同的数据（每种情况下都有一定数量的特定于领域的集合） ，同时启用广泛的重点将是利用人类提供的数据和机器人本身收集的数据，从视觉观察中直接学习机器人新技能的方法。研究人员的目标是开发此类算法并收集和传播合适的数据集。如果成功，该项目可能会带来在不同的现实环境中控制机器人的新方法，以及可以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使用这些工具和资源。没有能力收集大型数据集利用可重复使用的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此类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用于机器人学习的新算法工具、新的数据收集方法，当然，研究人员的技术方法将集中在两个关键点上：数据重用的开发算法。哪个Thrust 2 本身将分为两个部分：第一部分专注于模仿学习，第二部分专注于强化学习。在 Thrust 2 中，目标是收集可被广泛的机器人研究有效利用的大型且可重复使用的数据集。 Thrust 2 的第一部分将侧重于开发用于大量数据收集的开源工具，第二部分将侧重于收集和传播数据集本身。该项目得到了跨部门基础研究的支持。机器人项目，由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (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影响审查进行评估，被认为值得支持标准。