NRI-Large: Collaborative Research: Purposeful Prediction: Co-robot Interaction via Understanding Intent and Goals

为了让机器人与人类合作，它们需要能够准确预测人类的意图和行动。人们的行动是有目的的：也就是说，他们为了实现长期目标而做出一系列决定。例如，在开车从家里到商店时，人们会仔细规划一系列的道路，以便高效地到达那里。在预测一个人的下一个决定时，必须开发出反映这些有目的的行动的算法。目前，机器人无法预测人类的需求和目标，这是它们在家庭和工作场所大规模部署的根本障碍。这个项目的目的是开发一种新的有目的的预测科学，可以应用于跨越广泛领域的人与机器人的交互。这项工作借鉴了基于逆最优控制和逆平衡理论的最新技术，这些技术能够对观察到的故意行为进行统计上合理的推理。这些新方法提供了一个理论框架的基础，该框架将最优控制、搜索和规划等传统决策技术与概率方法相结合，这些方法对不确定性和隐藏信息进行推理，特别是关于目标、效用和意图的信息。智力优势：该项目将提供一个通用框架，允许机器人根据感知线索预测并适应人类同事的活动。研究人员将开发这一理论、一个计算工具箱，并与工业合作伙伴合作，在从计算机视觉到运动控制的各种机器人领域预测人类行为的这些新方法的原型部署。该项目具有变革性，因为它结合了一个新的理论/算法框架，并在数据量和许多应用程序的验证基础设施方面提供了广泛支持。更广泛的影响：家庭和工作场所的个人机器人革命取决于预测人类活动和意图的能力；中小型制造业将通过智能到足以理解并帮助同事执行灵活组装任务的敏捷机器人系统实现飞跃；强大的行人和车辆交通流量模型将使驾驶员预警系统和更安全的自主移动机器人成为可能。有目的的预测技术是实现对这些领域中的行动和意图的理解的重要一步。研究工作将包括在这一新兴的多学科研究领域对本科生、硕士和博士后研究员进行培训和指导，该领域位于计算机、认知科学和机器人学的交叉点。

In order for robots to collaborate with humans, they need to be able to accurately forecast human intent and action. People act with purpose: that is, they make sequences of decisions to achieve long-term objectives. For instance, in driving from home to a store, people carefully plan a sequence of roads that will get them there efficiently. In predicting a person's next decision, algorithms must be developed that reflect these purposeful actions. Currently, robots are unable to anticipate human needs and goals, and this represents a fundamental barrier to their large-scale deployment in the home and workplace. The aim of this project is to develop a new science of purposeful prediction that can be applied to human-robot interaction across a wide variety of domains. The work draws on recent techniques based on Inverse Optimal Control and Inverse Equilibria Theory that enable statistically sound reasoning about observed deliberate behavior. These new methods provide the foundations of a theoretical framework that integrates traditional decision making techniques like optimal control, search and planning with probabilistic methods that reason about uncertainty and hidden information, particularly about goals, utility and intent. Intellectual merit: The project will provide a general framework that allows robots to anticipate and adapt to the activities of their human co-workers based on perceptual cues. The investigators will develop the theory, a computational toolbox, and, in collaboration with industrial partners, prototype deployments of these new methods for the prediction of peoples' behavior in a diverse set of robotics domains from computer vision to motor control. The project is transformative in that it combines a novel theoretical/algorithmic framework with extensive support in terms of volume of data and validation infrastructure in the context of many applications. Broader impacts: A revolution in personal robotics in both the home and workplace depends on the ability to forecast human activities and intents; small- and medium- scale manufacturing will make a leap forward through agile robotic systems intelligent enough to understand and assist their co-workers in flexible assembly tasks; and robust models of pedestrian and vehicular traffic flow will enable more effective driver warning systems and safer autonomous mobile robots. Purposeful prediction technology is an important step towards enabling such understanding of actions and intents in these arenas. The research work will involve the training and mentoring of undergraduate, masters and doctoral students as well as post-doctoral fellows in this emerging multi-disciplinary research area at the intersection of computer and cognitive sciences and robotics.