In today’s era, robots are increasingly being developed to collaborate with human teammates in order to form highly efficient teams. However, it is not uncommon for teammates to prioritize their personal goals over assisting others in order to meet deadlines. Sometimes, the most successful teams are characterized by the ability of each team member to prioritize their own individual or shared objectives while maintaining consistency and cooperation with one another. To gain insights into human perceptions of robot teammates who prioritize their own tasks over helping human partners, we conducted a research experiment involving (n=317) participants. Through careful observation of various instances of prioritization, our findings indicate that humans are required to put in additional effort in tasks when the robots ruthlessly prioritize their own. In addition to that the humans feel more neglected by robots in verbal prioritization conditions, but view them as more intelligent and value their reliability and competitive capabilities in other scenarios.

Interactive intelligent systems are increasingly being deployed in safety critical contexts like Space Exploration. For humans to safely and successfully complete collaborative tasks with robots in these contexts, they must maintain Situational Awareness of their task context without being cognitively overloaded -- regardless of whether they are co-located with robots or interacting with them from a distance of thousands or millions of miles. In this paper, we present a novel autonomy design strategy we term Performative Autonomy, in which robots behave as if they have a lower level of autonomy than they are truly capable of (i.e., asking for advice they do not believe they truly need), for the sole purpose of maintaining interactants' Situational Awareness. In our first experiment (n=264), we begin by demonstrating that Performative Autonomy can increase Situational Awareness (SA) without overly increasing workload, and that this is true across tasks with different baseline levels of Mental Workload. In our second experiment (n=318), we consider cases where robots do not believe they need advice, but in fact have faulty perception or decision making capabilities. In this experiment, we only observed benefits to Performative Autonomy for specific types of questions, and only when there was significant cognitive load imposed by a secondary task; yet we observed uniform benefit on task performance for asking these types of questions regardless of task-imposed Mental workload. Our results from these two studies (total n=582) thus provide strong support for using this autonomy design strategy in future safety-critical missions as humanity explores the Moon, Mars, and beyond.

This paper introduces and justifies (through an n=210 online human-subject study) Deconstructed Trustee Theory, a theory of human-robot trust that factors the representation of trustee into robot body and robot identity in order to differentially model perceived trustworthiness of robot body and identity. This theory predicts (a) that different levels of trustworthiness can be attributed to a robot body and a robot identity, (b) that divergence between levels of perceived trustworthiness of body and identity may be effected by communication policies that reveal the potential for phenomena such as re-embodiment, co-embodiment, and agent migration in multi-robot systems, and (c) that perceived trustworthiness of body and identity may further diverge and be refined through moral cognitive processes triggered on observation of blameworthy actions.

The researchers in this study have developed a novel approach using mutual reinforcement learning (MRL) where both the robot and human act as empathetic individuals who function as reinforcement learning agents for each other to achieve a particular task over continuous communication and feedback. This shared model not only has a collective impact but improves human cognition and helps in building a successful human-robot relationship. In our current work, we compared our learned reinforcement model with a baseline non-reinforcement and random approach in a robotics domain to identify the significance and impact of MRL. MRL contributed to improved skill transfer, and the robot was able successfully to predict which reinforcement behaviors would be most valuable to its human partners.

Robots are capable of training humans to achieve complex tasks, and their helpful feedback can lead to useful human-robot collaborations. In this research we present a reinforcement learning model influenced by human cognition which is repurposed to enhance human learning, investigate a robot's ability to encourage and motivate humans and improve their performance. During teaching the robot trades off between exploration and exploitation to understand the human perception and develop a successful motivational approach. We compare our learned reinforcement model with a baseline nonreinforcement approach and with a random reinforcer, and achieve more effective teaching in the learned reinforcement condition. In addition, we discovered an extremely strong relationship (r = 0.88) between the robot's regret, in a machine learning sense, and the performance of its human partner.

Instructing human novices on complex tasks in non-standardized environments are an underexplored potential use for social co-robots, since instruction and skill transfer involving human experts can require an enormous commitment of time and resources. In this paper, we enable a humanoid Baxter robot to build a semantically accessible framework for task learning, teaching and representation via active learning with human experts using hierarchical semantic labels. This process not only helps the robot to learn tasks from expert demonstrations, but later improves the ability of the robot to teach novice human operators. Our results show that the better-understood learning from demonstration (LfD) task is greatly enhanced by the active learning and mutual semantic structure building in a expert-robot partnership, while the robot's ability to teach novices is improved, though the results are suggestive rather than conclusive at this point. We discuss the important aspects and power of learning and teaching from demonstration and how both benefit from communication and joint human-robot creation of semantic hierarchies.