Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems to be understandable to human users. The explainable AI (XAI) literature aims to enhance human understanding and human-AI team performance by providing users with necessary information about AI system behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including how to determine human informational needs, human workload, and human trust in autonomous systems. Drawing from the human factors literature, we propose the Situation Awareness Framework for Explainable AI (SAFE-AI), a three-level framework for the development and evaluation of explanations about AI system behavior. Our proposed levels of XAI are based on the informational needs of human users, which can be determined using the levels of situation awareness (SA) framework from the human factors literature. Based on our levels of XAI framework, we also suggest a method for assessing the effectiveness of XAI systems. We further detail human workload considerations for determining the content and frequency of explanations as well as metrics that can be used to assess human workload. Finally, we discuss the importance of appropriately calibrating user trust in AI systems through explanations along with other trust-related considerations for XAI, and we detail metrics that can be used to evaluate user trust in these systems.

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

Planning for complex scenarios, particularly in which large teams of humans with distributed expertise and varying preferences share a set of resources, poses a number of challenges including integrating distributed information and accounting for context-dependent preferences and constraints. We see three key pieces to solving the problem of introducing autonomous assistance through a mixed-initiative planning system in these scenarios: preference elicitation, integrating preferences into planning, and providing tailored explanations back to the humans in the loop. The process of preference elicitation, planning, and explanation can be integrated as an iterative process by which teams can efficiently converge on the ideal schedule. Linear Temporal Logic (LTL) is a common language, readily understandable by both planners and humans, that provides a natural link between the three components of the iterative planning problem, facilitating both elicitation of expressive preferences and intelligible explanations of the system's decision-making processes. Outputs of each of the preference elicitation, planning, and explanation pieces can be expressed as LTL specifications and used as inputs to each next step in the process. We propose to explore preference elicitation, planning, and explanation using LTL specifications and the integration of these pieces into an iterative process.

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 human-robot 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 human-robot 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.

Computational thinking is an important part of a modern education, and robotics provides a powerful tool for teaching programming logic in an interactive and engaging way. The robot garden presented in this paper is a distributed multi-robot system capable of running autonomously or under user control from a simple graphical interface. Over 100 origami flowers are actuated with LEDs and printed pouch motors, and are deployed in a modular array around additional swimming and crawling folded robots. The garden integrates state-of-the-art rapid design and fabrication technologies with distributed systems software techniques to create a scalable swarm in which robots can be controlled individually or as a group. The garden can be used to teach basic algorithmic concepts through its distributed algorithm demonstration capabilities and can teach programming concepts through its education-oriented user interface.