Mason Journals (George Mason Univ.)
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Integrating Machine Learning and Motion Planning Techniques for Multi-Object Search in Unknown Household Environments
No full textRobot navigation and object search within household environments are foundational tasks in the robotics community, yet made challenging as robots will often find themselves without a full map of the environment. The difficulty of these tasks increases when robots need to locate multiple objects or complete complex multi-stage objectives. To address this, we use a framework that translates complex input specifications into a structured sequence of goals and actions, which the robot uses to systematically search for objects in a specified order via an algorithm called PO-TLP. This approach allows for planning despite missing knowledge, leveraging "exploratory actions'' that define opportunities for the robot to discover necessary objects. Further, the PO-TLP planner integrates learning to predict where objects are likely to be found in unknown space and generates an efficient plan to look for missing task-relevant objects. Our research connects this PO-TLP algorithm to household domains by applying it to ProcTHOR, a tool for procedural generation of realistic home-like environments. To validate our approach, we performed a comprehensive analysis of planning costs associated with various PO-TLP implementations and their success in real-world scenarios. Our research applies an existing planning framework to a new application of open-set object search in household environments. By improving the performance and reliability of robotic systems in this domain, our work opens the door to new capabilities that were previously challenging in household domains, potentially leading to advancements in household robotics, automation, and beyond
A Preliminary Investigation of a Virtually Delivered Multimedia Essay Writing Strategy with College Students with Developmental Disabilities: Virtual Writing Strategy College Students With DD
No full text The authors of this study examined a virtually delivered multimedia expository writing strategy via a single case multiple-baseline across participants design with three college students enrolled in a postsecondary program for students with intellectual and developmental disabilities. Participants responded to expository essay prompts at the beginning of each virtual session. Two raters evaluated all baseline, intervention, and maintenance essay responses with a strategy rubric. Virtual one-to-one strategy instruction consisted of 45-min ZOOM sessions with live instruction and multimedia (e.g., animated videos, visual cues) content. Two out of three participants successfully applied strategy steps to construct and revise essay prompt responses
Virtual Reality for Teaching Science Vocabulary to Postsecondary Education Students with Intellectual Disability and Autism
No full textThe purpose of this study was to examine the use of virtual reality, an emerging technology, to teach college-age students with intellectual disability and autism to acquire science vocabulary words relating to human anatomy. One student with autism and two students with an intellectual disability participated in a multiple baseline across skills (i.e., acquisition of science vocabulary words) design. Data were collected on the three students' abilities to define and label three sets of human anatomy vocabulary words (i.e., bones, muscles, and organs) while using Organon 3D. Students used this application while using the Oculus Rift S, a virtual reality head-mounted display. Results indicated that all students acquired definitions and labeling knowledge for the new science vocabulary terms in the area of human anatomy. 
