Browsing by Author "Manner, Marie"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A Taxonomy for Task Allocation Problems with Temporal and Ordering Constraints
    (2016-05-06) Nunes, Ernesto; Manner, Marie; Mitiche, Hakim; Gini, Maria
    Previous work on assigning tasks to robots has proposed extensive categorizations of allocation of tasks with and without constraints.  The main contribution of this paper is a more specific categorization of problems that have both temporal and ordering constraints.  We propose a novel taxonomy that builds on the existing taxonomy for multi-robot task allocation and organizes the current literature according to the temporal nature of the tasks. We summarize widely used models and methods from the task allocation literature and related areas, such as vehicle routing and scheduling problems, showing similarities and differences.
  • Thumbnail Image
    Item
    Investigating Looking Behaviors with a Humanoid Robot
    (2018) Brockevelt, Kate; Manner, Marie; Richter, Nadja; Elison, Jed T
    Several research studies have shown that children with autism spectrum disorders (ASD) often display impairments in their ability to engage in many social behaviors that are crucial for the development of social-emotional competence, empathy, and expressive language. Because most children with autism show strong preferences for nonsocial information such as objects and machines (Adamson, Deckner, & Bakeman, 2010; Tapus et al., 2012), researchers have explored using humanoid robots to help children with autism develop skills for social interaction (Tapus et al., 2012). In this study, we used data from 55 typically developing toddlers (M= 33 months) who participated in a 10-minute semi-structured play session with a humanoid robot, the NAO V4 (Aldebaran Robotics). The NAO robot was pre-programmed to advance through seven structured social interactions, such as Simon Says, I Spy, a tai chi routine, and a dance to “If You’re Happy and You Know It.” Using this data, we examined children’s engagement with the robot, specifically their looking preferences during the interaction phases with the NAO.
  • Thumbnail Image
    Item
    Leveraging Computer Vision and Humanoid Robots to Detect Autism in Toddlers
    (2018-12) Manner, Marie
    Autism Spectrum Disorder is a developmental disorder often characterized by limited social skills, repetitive behaviors, obsessions, and/or routines. Early intervention significantly improves long-term outcomes for toddlers identified in the second year of life and is the best approach for affecting lasting positive change for children with an ASD. Research shows that children with autism especially enjoy technology, including autonomous (or seemingly autonomous) robots. Tying these together, we hypothesize that observing play interactions between very young children (2 - 4 years old) and a humanoid robot can help us identify children with autism; this first requires us to generate a very large, thoroughly characterized dataset of typically developing children. We begin with an eye tracking experiment comparing four different robots and a young human peer; this shows us which type of robot may be of most interest to children in an in-person, real-life play scenario, and if that robot is as interesting as a peer. Using the robot found to be most interesting in the eye tracking experiment, we next detail a human-robot interaction experiment that engages 2 - 4 year old children in a series of social games with a small humanoid robot; we then analyze the social distances, or proxemics, of the child throughout the interaction. To generate the proxemics data, we use a highly automated person detector which utilizes two state-of-the-art convolutional neural networks; with the proxemics and other development assessment data, we compare and group participants and discuss the implications of those results. A subset of robot interaction participants also finished the eye tracking task, so we discuss the relationship between the human-robot interactions and eye tracking results. Lastly, to validate the generalizability of our automated tracker, we test the system on two other child development experiments, a multiple-participant in-group bias play scenario for 5 and 8 year old children, and an unsolvable box task for toddlers.