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Successful collaboration requires team members to stay aligned, especially in complex sequential tasks. Team members must dynamically coordinate which subtasks to perform and in what order. However, real-world constraints like partial observability and limited communication bandwidth often lead to suboptimal collaboration. Even among expert teams, the same task can be executed in multiple ways. To develop multi-agent systems and human-AI teams for such tasks, we are interested in data-driven learning of multimodal team behaviors. Multi-Agent Imitation Learning (MAIL) provides a promising framework for data-driven learning of team behavior from demonstrations, but existing methods struggle with heterogeneous demonstrations, as they assume that all demonstrations originate from a single team policy. Hence, in this work, we introduce DTIL: a hierarchical MAIL algorithm designed to learn multimodal team behaviors in complex sequential tasks. DTIL represents each team member with a hierarchical policy and learns these policies from heterogeneous team demonstrations in a factored manner. By employing a distribution-matching approach, DTIL mitigates compounding errors and scales effectively to long horizons and continuous state representations. Experimental results show that DTIL outperforms MAIL baselines and accurately models team behavior across a variety of collaborative scenarios. 

Effective teamwork is crucial in high-stakes domains, yet it is highly challenging to achieve. Team members often must make decisions with limited information and under constraints on communication and time. Recognizing both the value of human coaches as well as the challenges of integrating them into practical settings, we envision AI-based coaching agents to enhance team coordination and performance. This extended abstract introduces AI Coaches and Coordinators, highlights key research questions from both human and AI perspectives that must be addressed to realize them, and summarizes our recent work in developing algorithms and systems to bring AI Coaches and Coordinators to fruition. 

The central line dressing change is a life-critical procedure performed by nurses to provide patients with rapid infusion of fluids, such as blood and medications. Due to their complexity and the heavy workloads nurses face, dressing changes are prone to preventable errors that can result in central line-associated bloodstream infections (CLABSIs), leading to serious health complications or, in the worst cases, patient death. In the post-COVID-19 era, CLABSI rates have increased, partly due to the heightened nursing workload caused by shortages of both registered nurses and nurse educators. To address this challenge, healthcare facilities are seeking innovative nurse training solutions to complement expert nurse educators. In response, we present the design, development and evaluation of a robotic tutoring system, ASTRID: the Automated Sterile Technique Review and Instruction Device. ASTRID, which is the outcome of a two-year participatory design process, is designed to aid in the training of nursing skills essential for CLABSI prevention. First, we describe insights gained from interviews with nurse educators and nurses, which revealed the gaps of current training methods and requirements for new training tools. Based on these findings, we outline the development of our robotic tutor, which interacts with nursing students, providing real-time interventions and summary feedback to support skill acquisition. Finally, we present evaluations of the system's performance and perceived usefulness, conducted in a simulated clinical setting with nurse participants. These evaluations demonstrate the potential of our robotic tutor in nursing education. Our work highlights the importance of participatory design for robotics systems, and motivates new avenues for foundational research in robotics. 

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork. 

AI-enabled agents designed to assist humans are gaining traction in a variety of domains such as healthcare and disaster response. It is evident that, as we move forward, these agents will play increasingly vital roles in our lives. To realize this future successfully and mitigate its unintended consequences, it is imperative that humans have a clear understanding of the agents that they work with. Policy summarization methods help facilitate this understanding by showcasing key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI. 

When faced with accomplishing a task, human experts exhibit intentional behavior. Their unique intents shape their plans and decisions, resulting in experts demonstrating diverse behaviors to accomplish the same task. Due to the uncertainties encountered in the real world and their bounded rationality, experts sometimes adjust their intents, which in turn influences their behaviors during task execution. This paper introduces IDIL, a novel imitation learning algorithm to mimic these diverse intent-driven behaviors of experts. Iteratively, our approach estimates expert intent from heterogeneous demonstrations and then uses it to learn an intent-aware model of their behavior. Unlike contemporary approaches, IDIL is capable of addressing sequential tasks with high-dimensional state representations, while sidestepping the complexities and drawbacks associated with adversarial training (a mainstay of related techniques). Our empirical results suggest that the models generated by IDIL either match or surpass those produced by recent imitation learning benchmarks in metrics of task performance. Moreover, as it creates a generative model, IDIL demonstrates superior performance in intent inference metrics, crucial for human-agent interactions, and aptly captures a broad spectrum of expert behaviors. 

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. 

Policy summarization is a computational paradigm for explaining the behavior and decision-making processes of autonomous robots to humans. It summarizes robot policies via exemplary demonstrations, aiming to improve human understanding of robotic behaviors. This understanding is crucial, especially since users often make critical decisions about robot deployment in the real world. Previous research in policy summarization has predominantly focused on simulated robots and environments, overlooking its application to physically embodied robots. Our work fills this gap by combining current policy summarization methods with a novel, interactive user interface that involves physical interaction with robots. We conduct human-subject experiments to assess our explanation system, focusing on the impact of different explanation modalities in policy summarization. Our findings underscore the unique advantages of combining virtual and physical training environments to effectively communicate robot behavior to human users. 

Light‐responsive materials enable the development of soft robots that are controlled remotely in 3D space and time without the need for cumbersome wires, onboard batteries, or altering the local environment. Azobenzene liquid crystal polymer networks are one such material that can move and deform in response to light actuation. Previous works have demonstrated azo‐based soft robotic grippers and transporters that are remotely powered by light. However, highly adaptive, automated spatiotemporal optical control over these materials has not yet been realized. Herein, a system for an azobenzene liquid crystal elastomer soft robotic arm is created by dynamically patterning light for independently maneuverable joints. The nonlinear material response to optical actuation is characterized, and the broad actuation space is explored with diverse arm configurations. A neural network is trained on the arm configurations and corresponding laser pattern to automate the pattern generation for a desired configuration. Finally, the azobenzene liquid crystal elastomer arm demonstrates complex targeted motion, marking an important step toward optically actuated soft robotics with applications ranging from optomechanics to biomedical tools.