CAREER: Robust and Autonomous Robot Adaptation in Novel Scenarios

职业：新场景中鲁棒且自主的机器人适应

• 批准号: 2237693
• 负责人: Chelsea Finn
• 金额: $ 60万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237693&HistoricalAwards=false
• 关键词: CAREER; Robust; Autonomous; Robot; Adaptation

A major current technological challenge is to enable deployment of robots into open real-world environments to help advance human welfare. This Faculty Early Career Development (CAREER) project will serve this goal while promoting scientific progress by studying how robots can adapt to new and unfamiliar surroundings encountered during deployment. Adaptation is needed for handling open-world environments since it is remarkably challenging to foresee all the possible scenarios that a robot may encounter. Adaptation demands a degree of autonomy and robustness. This project will study how robots can autonomously adapt during deployment, identify when and how to ask a human for help, and avoid catastrophic failures or getting perpetually stuck in place. The outcomes of this project are expected to have the potential to significantly expand the set of practical applications of robotics, including in manufacturing settings when there is variability in parts and desired configurations, and in the service industry, such as in hospitals and homes where the environment changes frequently based on people’s behavior. This project will also support the development of freely available course lectures and course content pertaining to robotics and machine learning, as well as a mentoring program for undergraduates from groups that are underrepresented in STEM.The central objective of this project is to advance the capability of robots to adapt online during deployment. Robotic reinforcement learning systems can in principle be applied to enable online adaptation, but in practice they are currently ill-equipped to do so. This is because they require supervision and environment resets that are unavailable during deployment. These reinforcement learning systems also do not provide means for robots to identify failures and proactively request interventions in novel environments. This research will develop capabilities that address these challenges and integrate them into a single real robotic system. The research will advance our understanding of: (1) how robots can prepare for unknown situations; (2) how autonomy affects the performance of robotic learning systems; (3) how robots can detect and avoid failures and irreversible states even in new environments; and (4) when automated robot systems should seek external forms of supervision. The developed framework will be thoroughly evaluated on physical robot arms, testing the ability to adapt to a wide variety of unseen circumstances, including new object poses, object materials, and object shapes.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.

当前的一个主要技术挑战是将机器人部署到开放的现实世界环境中，以帮助促进人类福祉，该学院的早期职业发展（CAREER）项目将服务于这一目标，同时通过研究机器人如何适应新的和陌生的环境来促进科学进步。应对开放世界环境需要适应，因为适应机器人可能遇到的所有情况都需要一定程度的自主性和鲁棒性，该项目将研究机器人如何在部署过程中自主适应。确定何时以及如何向人类寻求帮助，并避免灾难性故障或永远陷入困境，该项目的成果预计将有可能显着扩展机器人技术的实际应用范围，包括在存在可变性的制造环境中。在零件和所需的配置中，以及在服务行业中，例如在医院和家庭中，环境经常根据人们的行为而变化。该项目还将支持与机器人和机器学习有关的免费课程讲座和课程内容的开发，以及指导计划该项目的核心目标是提高机器人在部署过程中在线适应的能力。机器人强化学习系统原则上可以用于实现在线适应，但在实践中它们目前表现不佳。 -这是因为它们需要在部署期间无法进行的监督和环境重置，而且这些强化学习系统也无法为机器人提供识别故障并主动请求在新环境中进行干预的能力。这些挑战并将其融入该研究将增进我们对以下方面的理解：（1）机器人如何为未知情况做好准备；（3）机器人如何检测和避免故障和不可逆转的情况；甚至在新环境中也指出；（4）自动化机器人系统何时应寻求外部形式的监督，将在物理机器人手臂上进行彻底评估，测试适应各种看不见的环境的能力，包括新的物体姿势。 、物体材料和物体形状。这个奖项通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。