Awareness of team behaviors (e.g., individual activities and team intents) plays a critical role in human–robot teaming. Autonomous robots need to be aware of the overall intent of the team they are collaborating with in order to effectively aid their human peers or augment the team’s capabilities. Team intents encode the goal of the team, which cannot be simply identified from a collection of individual activities. Instead, teammate relationships must also be encoded for team intent recognition. In this article, we introduce a novel representation learning approach to recognizing team intent awareness in real-time, based upon both individual human activities and the relationship between human peers in the team. Our approach formulates the task of robot learning for team intent recognition as a joint regularized optimization problem, which encodes individual activities as latent variables and represents teammate relationships through graph embedding. In addition, we design a new algorithm to efficiently solve the formulated regularized optimization problem, which possesses a theoretical guarantee to converge to the optimal solution. To evaluate our approach’s performance on team intent recognition, we test our approach on a public benchmark group activity dataset and a multisensory underground search and rescue team behavior dataset newly collected from robots in an underground environment, as well as perform a proof-of-concept case study on a physical robot. The experimental results have demonstrated both the superior accuracy of our proposed approach and its suitability for real-time applications on mobile robots.

团队行为（例如，个人活动和团队意图）的意识在人机协作中起着至关重要的作用。自主机器人需要了解与他们合作的团队的整体意图，以便有效地帮助他们的人类同伴或增强团队的能力。团队意图编码团队的目标，不能简单地从单个活动的集合中识别。相反，还必须对队友关系进行编码以识别团队意图。在本文中，我们介绍了一种新颖的表示学习方法，以基于个人人类活动和团队中人类同行之间的关系，实时识别团队意图意识。我们的方法将用于团队意图识别的机器人学习任务制定为联合正则化优化问题，它将个人活动编码为潜在变量，并通过图嵌入表示团队成员关系。此外，我们设计了一种新算法来有效解决公式化的正则化优化问题，该算法具有收敛到最优解的理论保证。为了评估我们的方法在团队意图识别方面的表现，我们在公共基准群体活动数据集和从地下环境中的机器人新收集的多感官地下搜救队行为数据集上测试了我们的方法，并进行了概念验证物理机器人的案例研究。实验结果证明了我们提出的方法的优越准确性及其对移动机器人实时应用的适用性。