Rapidly Built Medical Crash Cart! Lessons Learned and Impacts on High-Stakes Team Collaboration in the Emergency Room Abstract --Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting and space exploration, autonomous robots that aid high-stakes teamwork remain underexplored. T o address this gap, we conducted a rapid prototyping process to develop a series of seemingly autonomous robot designed to assist clinical teams in the Emergency Room. We transformed a standard crash cart--which stores medical equipment and emergency supplies into a medical robotic crash cart (MCCR). The MCCR was evaluated through field deployments to assess its impact on team workload and usability, identified taxonomies of failure, and refined the MCCR in collaboration with healthcare professionals. By publicly disseminating our MCCR tutorial, we hope to encourage HRI researchers to explore the design of robots for high-stakes teamwork. Teleoperated robots have become indispensable tools for action teams--highly skilled specialist teams that collaborate in short, high-pressure events, requiring improvisation in unpredictable situations [1]. For example, disaster response teams rely on teleoperated robots and drones to aid search and rescue operations [2], [3]. High-stakes military and SW A T teams use teleoperated ordnance disposal [4] and surveillance robots [5] to keep the teams safe. Surgical teams employ teleoperated robots to perform keyhole surgeries with a level of precision that would be unimaginable without these machines [6], [7]. We built three teleoperated medical crash cart robots (MCCRs). MCCR 1 delivers supplies using a hoverboard circuit. MCCR 2 delivers supplies, recommends supplies using drawer opening capabilities, and was deployed at a medical training event which revealed insights.

It is known that the classical least square regression models achieve the optimal efficiency when the noises are Gaussian, however, they always underperform if the data is contaminated by non-Gaussian noises or outliers. Some robust regression models have been well developed in the past decades such as the median regression, the modal regression, the Huber regression and the least trimmed squares regression, etc. Moreover, a new robust regression model named the maximum correntropy criterion regression (MCCR) has been theoretically studied within the frame of statistical learning in Feng et al. (2015). Correntropy is constructed based on a kernel function and it is a generalized similarity measure between two random variables (see Santamar ıa et al. (2006); Gunduz and Principe (2009); Liu et al. (2007); He et al. (2011); Chen and Pr ıncipe (2012) 1

Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively explored and studied. Its application to regression problems leads to the robustness enhanced regression paradigm -- namely, correntropy based regression. Having drawn a great variety of successful real-world applications, its theoretical properties have also been investigated recently in a series of studies from a statistical learning viewpoint. The resulting big picture is that correntropy based regression regresses towards the conditional mode function or the conditional mean function robustly under certain conditions. Continuing this trend and going further, in the present study, we report some new insights into this problem. First, we show that under the additive noise regression model, such a regression paradigm can be deduced from minimum distance estimation, implying that the resulting estimator is essentially a minimum distance estimator and thus possesses robustness properties. Second, we show that the regression paradigm, in fact, provides a unified approach to regression problems in that it approaches the conditional mean, the conditional mode, as well as the conditional median functions under certain conditions. Third, we present some new results when it is utilized to learn the conditional mean function by developing its error bounds and exponential convergence rates under conditional $(1+\epsilon)$-moment assumptions. The saturation effect on the established convergence rates, which was observed under $(1+\epsilon)$-moment assumptions, still occurs, indicating the inherent bias of the regression estimator. These novel insights deepen our understanding of correntropy based regression, help cement the theoretic correntropy framework, and also enable us to investigate learning schemes induced by general bounded nonconvex loss functions.

In recent years, correntropy and its applications in machine learning have been drawing continuous attention owing to its merits in dealing with non-Gaussian noise and outliers. However, theoretical understanding of correntropy, especially in the statistical learning context, is still limited. In this study, within the statistical learning framework, we investigate correntropy based regression in the presence of non-Gaussian noise or outliers. To this purpose, we first introduce mixture of symmetric stable noise, which include Gaussian noise, Cauchy noise, and the mixture of Gaussian noise as special cases, to model non-Gaussian noise and outliers. We demonstrate that under the mixture of symmetric stable noise assumption, correntropy based regression can learn the conditional mean function or the conditional median function well without requiring the finite variance assumption of the noise. In particular, we establish learning rates for correntropy based regression estimators that are asymptotically of type $\mathcal{O}(n^{-1})$. We believe that the present study completes our understanding towards correntropy based regression from a statistical learning viewpoint, and may also shed some light on robust statistical learning for regression.