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

该项目的目标是创造方法，使机器人能够在与其他机器人或人类近距离移动和交流。在这些环境中，机器人必须了解自己的行为可能会如何影响和改变其他机器人和附近人的行为。这个项目的基本思想是让机器人在几种不同的策略中进行选择，选择最有可能在给定情况下工作得很好的一种。例如，机器人可能会决定转向右侧，因为它预测靠近的轮椅需要比典型的行人更大的空间。该项目还将调查机器人如何相互协调，决定哪些信息应该传输给队友机器人。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与普通人安全舒适地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。使用在线前转流程，从选项的“库”中评估候选策略。MPDM类型系统的核心问题是，更大的库允许更灵活的行为，但需要更多的计算资源。该项目以一种与以前的MPDM方法不同的方式实现了可表现性：它允许策略具有一个或多个连续参数，然后高效地计算这些连续参数的良好值。例如，虽然早期的MPDM工作可能有几个代表不同标称旅行速度的策略，但这项工作允许机器人设计者显式地将速度参数化。可以使用与深度学习网络中使用的方法类似的反向传播方法来调整该连续值参数。这种方法的主要优点是，单个策略可以生成更广泛的行为，从而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。这一奖项反映了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。

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.