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This paper addresses the problem of dynamic allocation of robot resources to tasks with hierarchical representations and multiple types of execution constraints, with the goal of enabling single-robot multitasking capabilities. Although the vast majority of robot platforms are equipped with more than one sensor (cameras, lasers, sonars) and several actuators (wheels/legs, two arms), which would in principle allow the robot to concurrently work on multiple tasks, existing methods are limited to allocating robots in their entirety to only one task at a time. This approach employs only a subset of a robot's sensors and actuators, leaving other robot resources unused. Our aim is to enable a robot to make full use of its capabilities by having an individual robot multitask, distributing its sensors and actuators to multiple concurrent activities. We propose a new architectural framework based on Hierarchical Task Trees that supports multitasking through a new representation of robot behaviors that explicitly encodes the robot resources (sensors and actuators) and the environmental conditions needed for execution. This architecture was validated on a two-arm, mobile, PR2 humanoid robot, performing tasks with multiple types of execution constraints. 

The research paper examines how engineering doctoral students describe their awareness and experiences with stress and mental health during their graduate studies. Despite the known bidirectional relationship between stress and mental health, there is limited research on how engineering doctoral students rationalize the disparity between the health consequences of chronic stress and the veneration of academic endurance in the face of these challenges. Given the dangers of chronic stress to physical and mental health, it is important to understand how students perceive the purpose and impact of stress and mental health within overlapping cultures of normalized stress. We conducted semi-structured interviews to understand participants' awareness, conceptualizations, and interpretations of stress and mental health. The research team analyzed interview transcripts using content analysis with inductive coding. Overall, we found that our participants recognized behavioral changes as an early sign of chronic stress while physical changes were a sign of sustained chronic stress; these cues signaled that participants needed additional support, including social support and campus mental health services. These findings support the need for greater mental health awareness and education within engineering doctoral programs to help students identify and manage chronic stress. 

Research demonstrates a growing mental health crisis in graduate education, which can contribute to productivity, departure, and well-being issues. To address this crisis and advocate for systemic change, this project explored faculty perceptions about graduate student mental health and how these perceptions intersect with direct action when student mental health challenges arise. We were guided by phenomenological inquiry to explore how faculty attitudes (n = 3) about mental health shape programmatic and individual decisions around supporting mental health. We thematically analyzed interviews discussing stress and mental health focused on faculty experiences. Faculty interviews demonstrated varying attitudes toward graduate student stress and mental health. Faculty desires to engage in discussions about stress or mental health were on a wide spectrum, often with work productivity guiding these discussions. Further, faculty highlighted levels of discomfort with engaging in discussions about mental health, especially with the students they work closest with. Findings indicate a need to foster faculty skill and comfort with engaging with students about their mental health while also providing clear institutional policies that support these actions to address the mental health crisis. 

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying “what to do” in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make “tea” and “sandwich”. We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says “I am thirsty” or “It is cold outside” the robot will start to perform the tea-making skill. In contrast, if the person says, “I am hungry” or “I need something to eat”, the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. 

In this work we address the System-of-Systems reassembling operation of a marsupial team comprising a hybrid Unmanned Aerial Vehicle and a Legged Locomotion robot, relying solely on vision-based systems and assisted by Deep Learning. The target application domain is that of large-scale field surveying operations under the presence of wireless communication disruptions. While most real-world field deployments of multi-robot systems assume some degree of wireless communication to coordinate key tasks such as multi-agent rendezvous, a desirable feature against unrecoverable communication failures or radio degradation due to jamming cyber-attacks is the ability for autonomous systems to robustly execute their mission with onboard perception. This is especially true for marsupial air / ground teams, wherein landing onboard the ground robot is required. We propose a pipeline that relies on Deep Neural Network-based Vehicle-to-Vehicle detection based on aerial views acquired by flying at typical altitudes for Micro Aerial Vehicle-based real-world surveying operations, such as near the border of the 400ft Above Ground Level window. We present the minimal computing and sensing suite that supports its execution onboard a fully autonomous micro-Tiltrotor aircraft which detects, approaches, and lands onboard a Boston Dynamics Spot legged robot. We present extensive experimental studies that validate this marsupial aerial / ground robot’s capacity to safely reassemble while in the airborne scouting phase without the need for wireless communication. 

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.