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

NRI-Large：协作研究：有目的的预测：通过理解意图和目标进行协作机器人交互

• 批准号: 1227495
• 负责人: Martial Hebert
• 金额: $ 214.67万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1227495&HistoricalAwards=false
• 关键词: NRI; Large; Collaborative; Research; Purposeful

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.

为了让机器人与人类合作，它们需要能够准确地预测人类的意图和行动。人们的行为是有目的的：也就是说，他们做出一系列的决定来实现长期目标。例如，在开车从家里到商店，人们仔细规划了一系列的道路，使他们能够有效地到达那里。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行为的算法。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。该项目的目的是开发一种新的有目的的预测科学，可以应用于各种领域的人机交互。这项工作借鉴了基于逆最优控制和逆均衡理论的最新技术，这些技术能够对观察到的故意行为进行统计上的合理推理。这些新方法为一个理论框架提供了基础，该框架将传统的决策技术（如最优控制、搜索和规划）与概率方法（对不确定性和隐藏信息，特别是目标、效用和意图进行推理）相结合。智力优势：该项目将提供一个通用框架，使机器人能够根据感知线索预测和适应人类同事的活动。研究人员将开发理论、计算工具箱，并与工业伙伴合作，将这些新方法的原型部署用于从计算机视觉到电机控制的各种机器人领域的人类行为预测。该项目具有变革性，因为它将新颖的理论/算法框架与许多应用程序上下文中的数据量和验证基础设施方面的广泛支持相结合。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灵活的机器人系统实现飞跃，这些系统足够智能，可以理解并协助同事完成灵活的装配任务；稳健的行人和车辆交通流模型将使更有效的驾驶员警告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的行动和意图的理解的重要一步。研究工作将涉及在计算机与认知科学和机器人技术交叉的新兴多学科研究领域对本科生、硕士、博士生以及博士后进行培训和指导。