NRI: FND: Connected and Continuous Multi-Policy Decision Making

这个项目的目标是创造一种方法，使机器人能够在接近其他机器人或人类的地方移动和交流。在这些环境中，机器人必须了解它的行为如何可能影响和改变附近其他机器人和人的行为。这个项目的基本思想是让机器人在几种不同的策略之间进行选择，在给定的情况下选择最可能有效的策略。例如，机器人可能会决定转向右侧，因为它预测到靠近的轮椅比普通行人需要更多的空间。该项目还将研究机器人如何相互协调，决定哪些信息应该传递给队友机器人。这种类型的研究对于制造能够在家庭、学校和医院等日常环境中安全舒适地与普通人互动的机器人非常重要。该项目的技术方法是扩展一种称为多策略决策（MPDM）的规划算法。使用在线前展流程，从选项“库”中评估候选策略。MPDM类型系统的核心矛盾在于，较大的库允许更灵活的行为，但需要更多的计算资源。该项目以一种不同于以往MPDM方法的方式实现了表达性：它允许策略具有一个或多个连续参数，然后有效地计算这些连续参数的良好值。例如，早期的MPDM工作可能有几个代表不同名义行驶速度的策略，而这项工作允许机器人设计师显式地参数化速度。这个连续值参数可以使用类似于深度学习网络中使用的反向传播方法进行调整。这种方法的主要优点是单个策略可以生成更广泛的行为，这减少了必须明确考虑的策略的数量。反过来，这降低了规划过程的计算复杂性。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。

The goal of this project is to create methods that allow robots to move and communicate in close proximity to other robots or humans. In these settings, a robot must understand how its behavior is likely to influence and change the behavior of other robots and people nearby. The basic idea of this project is to allow the robot to select between several different strategies, picking the one that is most likely to work well in a given situation. For example, a robot might decide to veer towards the right because it predicts that an approaching wheelchair requires more room than a typical pedestrian. This project will also investigate how robots can coordinate with each other, deciding what information should be transmitted to teammate robots. This type of research is important in order to build robots that can safely and comfortably interact with regular people in everyday environments like their homes, schools, and hospitals. The technical approach of this project is to extend a planning algorithm known as Multi-Policy Decision Making (MPDM). Using an on-line forward roll-out process, candidate policies are evaluated from a "library" of options. The core tension in MPDM type systems is that larger libraries allow more flexible behaviors, but require greater computational resources. This project achieves expressivity in a different way than previous MPDM approaches: it allows policies to have one or more continuous parameters, and then efficiently computes good values of those continuous parameters. For example, whereas earlier MPDM work might have had several policies representing different nominal speeds of travel, this work allows robot designers to explicitly parameterize velocity. This continuous-valued parameter can be tuned using backpropagation methods similar to those used in deep learning networks. The key advantage of this approach is that a single policy can generate a wider range of behaviors, which reduces the number of policies that must be explicitly considered. In turn, this reduces the computational complexity of the planning process.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.