CAREER: Safe and Influencing Interactions for Human-Robot Systems

在研究与人互动的机器人系统时，关键挑战之一是无法获得人类行为的一般模型。人类通常不会遵循固定的模式。随着时间的推移，它们会相互变化，适应机器人。人类获得了经验--在多次互动后，人们与自动驾驶汽车互动时的驾驶行为将显著不同。在辅助机器人学中，人类的反应将随着机器人的适应而改变。自动驾驶汽车的路线决策会影响其他人类驾驶员的路线选择，并可能导致拥堵等不良的全球特性。这带来了一系列新的挑战，包括机器人应该如何规划安全可靠的战略，这些战略意识到它们对人和整个社会的影响。该项目为分析和规划人类与机器人之间的重复交互奠定了基础。我们的工作将通过在家庭、医院、仓库和智能城市等环境中增加对机器人与人类交互的理解，直接影响人类的舒适度、安全和公共生活。该项目的目标是集中于安全和交互机器人的关键组成部分之一--形式化影响交互，即影响人类反应的机器人动作。这需要开发人类行为的计算模型，并导致更好地理解和形式化地与机器人进行安全可靠的交互。本项目研究了三个主要挑战：1)人类建模：研究人员将开发数据高效的方法来学习与自治系统交互时人类行为的计算模型。人与机器人交互的挑战之一是缺乏来自人类的数据。这项工作开发了主动学习技术，可以智能地查询和整合从人类反馈中收集的不同类型的数据。2)影响互动：很明显，人类会受到彼此之间简单互动的影响，例如，如果人们去见一个总是迟到的朋友，他们就会计划迟到。同样，当人们与机器人互动时，他们的行为也会发生变化。如果他们看到一辆自动驾驶汽车多次被困在十字路口，他们会决定绕过它。该项目计划设计机器人算法，考虑它们对人类的影响，它们如何改变人类的行为，以及如何帮助整个系统。3)安全交互：当规划影响人类的交互时，机器人将依赖于学习的人类模型。然而，由于缺乏数据或模型参数不足，获得真正正确可靠的人体模型可能是一项挑战。该项目设计了经过验证的机器人政策，将对人体模型中存在的不准确保持稳健，并在长期互动中将整个系统带到理想的状态。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。

One of the key challenges when studying robotics systems that interact with people is the lack of access to a general model of how humans behave. Humans usually do not follow a fixed stationary model. They change and adapt to each other and to the robots over time. Humans gain experience - people's driving behavior when interacting with an autonomous car will be significantly different after many interactions. In assistive robotics, human responses will change as robots adapt. Routing decisions of autonomous cars influence other human drivers' routing choices and can result in undesirable global properties such as congestion. This introduces a new set of challenges including how robots should plan for safe and reliable strategies that are aware of their effects on people and the society as a whole. This project lays the foundations of analyzing and planning for repeated interactions between humans and robots. Our work will directly impact humans' comfort, safety, and public life by increasing robot understanding in interactions with humans in environments such as homes, hospitals, warehouses, and smart cities. The goal of this project is to focus on one of the key components of safe and interactive robotics -- formalizing influencing interactions, i.e., robot actions that influence human responses. This requires developing computational models of human behaviors, and leads to better understanding and formalisms for safe and reliable interactions with robots. This project investigates three main challenges: 1) human modeling: the investigator will develop data efficient methods to learn computational models of human behaviors while interacting with autonomous systems. One of the challenges in human-robot interaction is the lack of data from humans. This work develops active learning techniques that intelligently query and integrate different types of data collected from human feedback. 2) influencing interactions: it is clear that humans can be influenced by simple interactions with each other, e.g., people plan to arrive late if they are meeting a friend who is always late. Similarly, people's behavior changes when interacting with robots. If they observe an autonomous car being stuck at an intersection multiple times, they decide to navigate around it. This project plans to design robotics algorithms that are mindful of their effects on humans, how they can change the human behavior, and how that can help the overall system. 3) safe interactions: when planning for interactions that influence people, the robot will rely on learned human models. However, having access to a truly correct and reliable human model can be challenging due to lack of data or insufficient model parameters. This project designs verified robot policies that will be robust to inaccuracies present in human models, and takes the overall system to desirable states over long-term interactions.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.