By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.Would we trust a robot? Science fiction says no, but explainable robotics may find a way.Insects are a constant source of inspiration for roboticists. Their compliant bodies allow them to squeeze through small openings and be highly resilient to impacts. However, making subgram autonomous soft robots untethered and capable of responding intelligently to the environment is a long-standing challenge. One obstacle is the low power density of soft actuators, leading to small robots unable to carry their sense and control electronics and a power supply. Dielectric elastomer actuators (DEAs), a class of electrostatic electroactive polymers, allow for kilohertz operation with high power density but require typically several kilovolts to reach full strain. The mass of kilovolt supplies has limited DEA robot speed and performance. In this work, we report low-voltage stacked DEAs (LVSDEAs) with an operating voltage below 450 volts and used them to propel an insect-sized (40 millimeters long) soft untethered and autonomous legged robot. The DEAnsect body, with three LVSDEAs to drive its three legs, weighs 190 milligrams and can carry a 950-milligram payload (five times its body weight). The unloaded DEAnsect moves at 30 millimeters/second and is very robust by virtue of its compliance. The sub-500-volt operation voltage enabled us to develop 780-milligram drive electronics, including optical sensors, a microcontroller, and a battery, for two channels to output 450 volts with frequencies up to 1 kilohertz. By integrating this flexible printed circuit board with the DEAnsect, we developed a subgram robot capable of autonomous navigation, independently following printed paths. This work paves the way for new generations of resilient soft and fast untethered robots.Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.Growing interest in reinforcement learning approaches to robotic planning and control raises concerns of predictability and safety of robot behaviors realized solely through learned control policies. In addition, formally defining reward functions for complex tasks is challenging, and faulty rewards are prone to exploitation by the learning agent. Here, we propose a formal methods approach to reinforcement learning that (i) provides a formal specification language that integrates high-level, rich, task specifications with a priori, domain-specific knowledge; (ii) makes the reward generation process easily interpretable; (iii) guides the policy generation process according to the specification; and (iv) guarantees the satisfaction of the (critical) safety component of the specification. The main ingredients of our computational framework are a predicate temporal logic specifically tailored for robotic tasks and an automaton-guided, safe reinforcement learning algorithm based on control barrier functions. Although the proposed framework is quite general, we motivate it and illustrate it experimentally for a robotic cooking task, in which two manipulators worked together to make hot dogs.The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines. Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. selleck kinase inhibitor Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities.Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities.