To forge effective collaborations with humans, robots require the capacity to understand and predict the behaviors of their human counterparts. There is a growing body of computational research on human modeling for human-robot interaction (HRI). However, a key bottleneck in conducting this research is the relative lack of data of human internal states – like intent, workload, and trust – which undeniably affect human behavior. Despite their significance, these states are elusive to measure, making the assembly of datasets a challenge and hindering the progression of human modeling techniques. To help address this, we first introduce Rescue World for Teams (RW4T): a configurable testbed to simulate disaster response scenarios requiring human-robot collaboration. Next, using RW4T, we curate a multimodal dataset of human-robot behavior and internal states in dyadic human-robot collaboration. This RW4T dataset includes state, action and reward sequences, and all the necessary data to replay a visual task execution. It further contains psychophysiological metrics like heart rate and pupillometry, complemented by self-reported cognitive state measures. With data from 20 participants, each undertaking five human-robot collaborative tasks, this dataset accompanied with the simulator can serve as a valuable benchmark for human behavior modeling.

Offline imitation learning (IL) refers to learning expert behavior solely from demonstrations, without any additional interaction with the environment. Despite significant advances in offline IL, existing techniques find it challenging to learn policies for long-horizon tasks and require significant re-training when task specifications change. Towards addressing these limitations, we present GO-DICE an offline IL technique for goal-conditioned long-horizon sequential tasks. GO-DICE discerns a hierarchy of sub-tasks from demonstrations and uses these to learn separate policies for sub-task transitions and action execution, respectively; this hierarchical policy learning facilitates long-horizon reasoning. Inspired by the expansive DICE-family of techniques, policy learning at both the levels transpires within the space of stationary distributions. Further, both policies are learnt with goal conditioning to minimize need for retraining when task goals change. Experimental results substantiate that GO-DICE outperforms recent baselines, as evidenced by a marked improvement in the completion rate of increasingly challenging pick-and-place Mujoco robotic tasks. GO-DICE is also capable of leveraging imperfect demonstration and partial task segmentation when available, both of which boost task performance relative to learning from expert demonstrations alone.

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how human-computer interaction (HCI) and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 97 core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, usability , and human-AI collaboration performance . Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI.

Autonomous agents are increasingly being deployed amongst human end-users. Yet, human users often have little knowledge of how these agents work or what they will do next. This lack of transparency has already resulted in unintended consequences during AI use: a concerning trend which is projected to increase with the proliferation of autonomous agents. To curb this trend and ensure safe use of AI, assisting users in establishing an accurate understanding of agents that they work with is essential. In this work, we present AI teacher, a user-centered Explainable AI framework to address this need for autonomous agents that follow a Markovian policy. Our framework first computes salient instructions of agent behavior by estimating a user’s mental model and utilizing algorithms for sequential decision-making. Next, in contrast to existing solutions, these instructions are presented interactively to the end-users, thereby enabling a personalized approach to improving AI transparency. We evaluate our framework, with emphasis on its interactive features, through experiments with human participants. The experiment results suggest that, relative to non-interactive approaches, interactive teaching can both reduce the amount of time it takes for humans to create accurate mental models of these agents and is subjectively preferred by human users.

Motion planning is a core problem in robotics, with a range of existing methods aimed to address its diverse set of challenges. However, most existing methods rely on complete knowledge of the robot environment; an assumption that seldom holds true due to inherent limitations of robot perception. To enable tractable motion planning for high-DOF robots under partial observability, we introduce BLIND, an algorithm that leverages human guidance. BLIND utilizes inverse reinforcement learning to derive motion-level guidance from human critiques. The algorithm overcomes the computational challenge of reward learning for high-DOF robots by projecting the robot’s continuous configuration space to a motion-planner-guided discrete task model. The learned reward is in turn used as guidance to generate robot motion using a novel motion planner. We demonstrate BLIND using the Fetch robot and perform two simulation experiments with partial observability. Our experiments demonstrate that, despite the challenge of partial observability and high dimensionality, BLIND is capable of generating safe robot motion and outperforms baselines on metrics of teaching efficiency, success rate, and path quality.

Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members’ mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.