CAREER: Safe and Efficient Robot Learning from Demonstration in the Real World

未来几年，通用机器人将以前所未有的数量进入家庭和工作场所，但面临着定制化的重大挑战——在许多不同的非结构化环境中执行用户指定任务的能力。为了满足这种需求，机器人从演示中学习（LfD）已经成为一种范例，允许用户通过简单地向机器人展示如何执行任务而不是编写代码来快速自然地对机器人进行编程。这种方法的目的是允许非专业用户对机器人进行编程，以及交流难以转化为正式代码的具体知识。然而，目前最先进的LfD算法还没有为广泛部署做好准备，因为它们通常不可靠，需要太多的数据，并且在实验室环境中设计为单次学习。这项工作解决了这些问题，以帮助未来的机器人执行从家庭老年人护理到可重构制造的重要任务。具体来说，这项工作确定了当前LfD算法在应用于现实世界之前需要进行的三个重大技术改进：安全保证的需要，从非常有限的数据中学习的能力，以及以持续的、终身的方式不断改进的能力。开发了安全LfD的正式理论，以及提供代理性能强概率下界的实用算法。算法效率通过重新检查常见的统计假设（如独立和相同分布的数据）和使用多模态侧信息（如自然语言和凝视）来解决。最后，使用主动学习策略和人类信念建模来实现交互式、持续学习。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

General purpose robots are poised to enter the home and workplace in unprecedented numbers in coming years, but face the significant challenge of customization - the ability to perform user-specified tasks in many different unstructured environments. In response to this need, robot learning from demonstration (LfD) has emerged as a paradigm that allows users to quickly and naturally program robots by simply showing them how to perform a task, rather than by writing code. This methodology aims to allow non-expert users to program robots, as well as communicate embodied knowledge that is difficult to translate into formal code. However, current state-of-the-art LfD algorithms are not yet ready for widespread deployment, as they are often unreliable, need too much data, and are designed to learn in a single session in a laboratory setting. This work addresses these issues to help enable future robots to perform important tasks ranging from in-home elderly care to reconfigurable manufacturing.Specifically, this work identifies three significant technical improvements to current LfD algorithms that are needed before they can be deployed in the real world: the need for safety guarantees, the ability to learn from very limited amounts of data, and the ability to continually improve in an ongoing, life-long fashion. A formal theory of safe LfD is developed, along with practical algorithms that provide strong probabilistic lower bounds on agent performance. Algorithmic efficiency is addressed via a re-examining of common statistical assumptions (such as independent and identically distributed data) and through the use of multimodal side-information, such as natural language and gaze. Finally, active learning strategies and modeling of human beliefs are used to enable interactive, continual learning.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.