NSF-BSF: RI: Small: Learning to plan safely

机器人和自动驾驶车辆，为了实现它们被赋予的目标，会制定计划，指定要采取的行动。这种自动规划的一个主要障碍是，它需要对机器人或自动驾驶车辆运行的环境进行描述，并具有足够的保真度，以准确预测这些行动的结果。这种对环境的准确描述很难用手写出来，因此已经提出了机器学习技术来根据对环境的观察自动构建这样的描述。到目前为止，这些方法通常没有为学习到的描述的准确性提供任何保证。这使得机器人或自动驾驶车辆的行为可能会产生意想不到的后果。特别是，它的行动可能违反旨在确保其安全运营的特定目标的部分内容。该项目将开发学习环境描述的方法，使自动化规划方法能够保证所产生的计划满足规定的目标。该项目调查规划代理人如何通过使用计划执行的例子来改进其世界模型，以了解行动的效果以及何时应该采取这些行动。代理的目标是保证其操作是安全的，或者检测到所请求的操作是不可能保证的。研究小组将开发算法，利用提供的观测数据建立环境的部分近似模型，以及能够控制使用模型生成的计划的安全性和有效性的元模型。然后，该团队将开发自动计划算法，使用这些学习的模型来生成具有所需保证的计划。该项目将基于模型的方法和数据驱动的方法相结合，以生成“安全”的计划。当不能保证安全计划时，该项目的目标是使用可能近似正确(PAC)学习理论中的概念来量化生成的计划“近似安全”的概率。该项目将确定何时有可能保证计划成功，并将进一步确定在为更广泛的情况制定计划时容忍小的失败可能性的效果。这项研究预计将增加自动化规划可以应用的领域，包括更多难以获得世界模型和要求安全的情况。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。

Robots and autonomous vehicles, in order to achieve the goals they are given, create plans that specify what actions to take. A major impediment to this kind of automated planning is that it requires a description of the environment in which the robot or autonomous vehicle operates, of sufficient fidelity to accurately predict the outcome of those actions. Such accurate descriptions of an environment are difficult to write by hand, and so machine learning techniques have been proposed to automatically construct such descriptions from observations of the environment. These methods to-date have generally not provided any guarantees for the accuracy of the learned description. This leaves open the possibility that the actions of the robot or autonomous vehicle could have unintended consequences. In particular, its actions could violate portions of the specified objectives that were intended to ensure its operation is safe. This project will develop methods for learning descriptions of environments that enable the automated planning methods to guarantee that the resulting plan meets the specified objectives. The project investigates how a planning agent can improve its model of the world by using examples of plan executions to learn the effects of actions and when those actions should be taken. The goal is for the agent to either guarantee that its operation is safe or to detect that the requested operation is impossible to guarantee. The research team will develop algorithms that use the provided observations to build a partial, approximate model of the environment and a meta-model that enables control of the safety and effectiveness of plans produced using the models. The team will then develop automated planning algorithms that use these learned models to produce plans with the desired guarantees. The project combines model-based and data-driven methods to generate "safe" plans. When safe plans cannot be guaranteed, the project aims to quantify the probability that a generated plan is "approximately safe" using concepts from probably approximately correct (PAC) learning theory. The project will establish when it is possible to guarantee that plans will succeed, and will furthermore determine the effect of tolerating a small probability of failure in formulating plans for a broader range of circumstances. This research is expected to increase range of domains in which automated planning can be applied, including more situations in which world models are difficult to obtain and safety is a requirement.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.