Robotics researchers are increasingly exploring how robots can support human groups and teams. Action teams experience high workload, must work quickly, and must make decisions under uncertainty. Robots in these teams must be designed and contextualized to not contribute to errors or interrupt human team workflow. We conducted a study where human-robot action teams collaborated with a mobile manipulator (Stretch) in an escape room paradigm to better understand: 1) how a robot’s actions influenced intra-team dynamics, and 2) how attitudes towards the robot affected human-robot teaming. Our behavioral analysis highlights the effect of a robot’s functional and social behaviors on its acceptance within teams, how human teams adapt to perceived robot capabilities, and the significance of the robot’s nonverbal cues in shaping human expectations. These insights offer valuable implications for designing effective human-robot interactions in dynamic environments where team success can save lives.

Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot’s knowledge, capabilities, shortcomings, goals, and intentions. The research question that I investigate is “How can the robot help the human build a better mental model of the robot?”

Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. Various research communities are contributing methods for ITL and a large subset of this research is robot-centered with a focus on developing algorithms that can learn online, quickly. This paper studies the ITL problem from a human-centered perspective to provide guidance for robot design so that human teachers can naturally teach ITL robots. In this paper, we present 1) a qualitative bidirectional analysis of an interactive teaching study (N=10) through which we characterize various aspects of actions intended and executed by human teachers when teaching a robot; 2) an in-depth discussion of the teaching approach employed by two participants to understand the need for personal adaptation to individual teaching styles; and 3) requirements for ITL robot design based on our analyses and informed by a computational theory of collaborative interactions, SharedPlans.