Skill Interaction Categories for Communication in Flexible HumanRobot Teams Flexible humanrobot cooperation can be coordinated by dynamic task allocation, where team members observe task progress and repeatedly decide to execute outstanding operations from a commonly known task model. Such tasklevel coordination strategies do not cover cases, where a high level of abstraction for task modelling implies operations infeasible for one single agent due to limited capabilities. Tasklevel coordination can handle these operations by introducing additional operationlevel coordination for more finegrained interaction, e.g. to hand over a tool or provide a helping hand. We contribute an approach to classify operations regarding the interaction they require. Based on this classification, we show how robots can plan and issue communication to coordinate respective interaction within dynamic plan execution. Against the background of Smart Factories, a smartphone app is used as communication channel and evaluated in a user study.

A Taxonomy for Characterizing Modes of Interactions in Goaldriven, Humanrobot Teams As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addressed the task of characterizing humanrobot interaction from different perspectives and in multiple contexts. However, the numerous factors behind interaction work in conjunction, and the insights gained from one perspective can inadvertently affect another, creating a need for unification of these taxonomies and frameworks within an overarching taxonomy that systematically defines these relationships. In this paper we review existing taxonomies related to humanrobot interaction, the behavioral sciences, and social and algorithmic taxonomies, and propose an overarching ontology for the factors from these works. We identify three main components characterizing the structure of an interaction (environment, task, and team), and structure them over two levels: contextual factors and factors driven by local dynamics. Finally, we present an analysis of how these factors affect decisions about levels of robot automation and level of information abstraction in an interaction, and discuss curent gaps in the literature that can motivate future research.