CAREER: Robust and Autonomous Robot Adaptation in Novel Scenarios

当前的一个主要技术挑战是使机器人能够部署到开放的现实世界环境中，以帮助增进人类福利。这个学院早期职业发展(CALEAR)项目将通过研究机器人如何适应部署过程中遇到的新的和不熟悉的环境来服务于这一目标，同时促进科学进步。处理开放世界的环境需要适应，因为预测机器人可能遇到的所有可能场景是非常具有挑战性的。适应需要一定程度的自主性和稳健性。这个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，并避免灾难性的故障或永远被困在原地。该项目的成果有望显著扩大机器人的实际应用范围，包括在零件和所需配置存在变化的制造环境中，以及在服务行业中，例如在医院和家庭中，环境根据人的行为频繁变化。该项目还将支持开发与机器人学和机器学习有关的免费课程讲座和课程内容，以及为STEM中代表性不足的群体的本科生提供指导计划。该项目的中心目标是提高机器人在部署期间在线适应的能力。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，它们目前缺乏这样做的装备。这是因为它们需要在部署期间不可用的监管和环境重置。这些强化学习系统也没有为机器人提供在新环境中识别故障和主动请求干预的手段。这项研究将开发解决这些挑战的能力，并将它们集成到一个真正的机器人系统中。这项研究将促进我们对以下问题的理解：(1)机器人如何为未知情况做准备；(2)自主性如何影响机器人学习系统的性能；(3)即使在新的环境中，机器人如何检测和避免故障和不可逆状态；以及(4)自动化机器人系统何时应该寻求外部形式的监督。开发的框架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。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。

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.