Challenges in Collaborative HRI for Remote Robot Teams

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019 Workshop: The Challenges of Working on Social Robots that Collaborate with People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, U

A Demonstration of Continuous Interaction with Elckerlyc

We discuss behavior planning in the style of the SAIBA framework for continuous (as opposed to turn-based) interaction. Such interaction requires the real-time application of minor shape or timing modifications of running behavior and anticipation of behavior of a (human) interaction partner. We discuss how behavior (re)planning and on-the-fly parameter modification fit into the current SAIBA framework, and what type of language or architecture extensions might be necessary. Our BML realizer Elckerlyc provides flexible mechanisms for both the specification and the execution of modifications to running behavior. We show how these mechanisms are used in a virtual trainer and two turn taking scenarios

Smart Exposition Rooms: The Ambient Intelligence View

We introduce our research on smart environments, in particular research on smart meeting rooms and investigate how research approaches here can be used in the context of smart museum environments. We distinguish the identification of domain knowledge, its use in sensory perception, its use in interpretation and modeling of events and acts in smart environments and we have some observations on off-line browsing and on-line remote participation in events in smart environments. It is argued that large-scale European research in the area of ambient intelligence will be an impetus to the research and development of smart galleries and museum spaces

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior