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In this work we address the flexible physical docking-and-release as well as recharging needs for a marsupial system comprising an autonomous tiltrotor hybrid Micro Aerial Vehicle and a high-end legged locomotion robot. Within persistent monitoring and emergency response situations, such aerial / ground robot teams can offer rapid situational awareness by taking off from the mobile ground robot and scouting a wide area from the sky. For this type of operational profile to retain its long-term effectiveness, regrouping via landing and docking of the aerial robot onboard the ground one is a key requirement. Moreover, onboard recharging is a necessity in order to perform systematic missions. We present a framework comprising: a novel landing mechanism with recharging capabilities embedded into its design, an external battery-based recharging extension for our previously developed power-harvesting Micro Aerial Vehicle module, as well as a strategy for the reliable landing and the docking-and-release between the two robots. We specifically address the need for this system to be ferried by a quadruped ground system while remaining reliable during aggressive legged locomotion when traversing harsh terrain. We present conclusive experimental validation studies by deploying our solution on a marsupial system comprising the MiniHawk micro tiltrotor and the Boston Dynamics Spot legged robot. 

Mental health is a key attribute for success in graduate programs. However, previous studies demonstrate a growing mental health crisis in graduate education, which can contribute to issues with productivity, departure, and well-being. Engineering students are not immune to this crisis, yet are one of the least likely disciplines to seek help for mental health. Despite this trend, there is limited literature available to provide evidence-based practices for addressing the causes and persistence of mental health issues for engineering graduate students. To address this need and to begin advocating for systemic change, this project will explore how faculty and student attitudes about mental health intersect with the institutional features that direct action when a mental health crisis arises. Specifically, this project focuses on generating new knowledge about the ways faculty and students conceptualize mental health within engineering graduate programs. Understanding these facets of mental health in academia is a first step toward changing policies and practices that have perpetuated the mental health crisis in engineering. This long-term outcome of this EEC project will develop evidence-based practices to improve student mental health services in graduate engineering programs. 

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots’ long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.Our prior Socially-Aware Navigation model considered con-text classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot’s navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment. 

Homophily, a person's bias for having ties with people who are similar to themselves in social ways, has a vital role in creating a social connection between people. Studying homophily in human-robot interactions can provide valuable insights for improving those interactions. In this paper, we investigate whether similar interests have a positive effect on a human-robot interaction similar to the positive impact it can have on human-human interaction. We explore whether sharing similar interests can affect trust. This experiment consisted of two NAO robots; each gave differing speeches. For each participant, their national origin was asked in the pre-questionnaire, and during the sessions, one of the robot's topics was either personalized or not to their national origin. Since one robot shared a familiar topic, we expected to observe bonding between humans and the robot. We gathered data from a post-questionnaire and analyzed them. The results summarize the hypotheses here. We conclude that homophily plays a significant role in human-robot interaction, affecting trust in a robot partner. 

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot could select and account for social objectives related to navigation autonomously. 

Robots’ autonomous navigation in public spaces and their social awareness suited to the environmental context is an active investigation in HRI. In this paper, we are presenting a methodology to achieve this goal. While most navigation models focus on objects, context, or human presence in the scene, we will incorporate all three to perceive the environment more accurately. Other than scene perception, the other important aspect of socially aware navigation is the social norms associated with the context. To do so, we have included interviews with museum visitors, volunteers, and staff to gather information about museums and convert the text data to social rules. This effort is currently in progress, we present a framework for future study and analysis of this problem. 

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN). 

As Human-Robot Interaction becomes more sophisticated, measuring the performance of a social robot is crucial to gauging the effectiveness of its behavior. However, social behavior does not necessarily have strict performance metrics that other autonomous behavior can have. Indeed, when considering robot navigation, a socially-appropriate action may be one that is sub-optimal, resulting in longer paths, longer times to get to a goal. Instead, we can rely on subjective assessments of the robot's social performance by a participant in a robot interaction or by a bystander. In this paper, we use the newly-validated Perceived Social Intelligence (PSI) scale to examine the perception of non-humanoid robots in non-verbal social scenarios. We show that there are significant differences between the perceived social intelligence of robots exhibiting SAN behavior compared to one using a traditional navigation planner in scenarios such as waiting in a queue and group behavior. 

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.