(Georgia Institute of Technology, 2009-05)
Trevor, Alexander J. B.; Park, Hae Won; Howard, Ayanna M.; Kemp, Charles C.
When young children play, they often manipulate toys that have been specifically designed to accommodate and stimulate their perceptual-motor skills. Robotic playmates capable of physically manipulating toys have the potential to engage children in therapeutic play and augment the beneficial interactions provided by overtaxed care givers and costly therapists. To date, assistive robots for children have almost exclusively focused on social interactions and teleoperative control. Within this paper we present progress towards the creation of robots that can engage children in manipulative play. First, we present results from a survey of popular toys for children under the age of 2 which indicates that these toys share simplified appearance properties and are designed to support a relatively small set of coarse manipulation behaviors. We then present a robotic control system that autonomously manipulates several toys by taking advantage of this consistent structure. Finally, we show results from an integrated robotic system that imitates visually observed toy playing activities and is suggestive of opportunities for robots that play with toys.
(Georgia Institute of Technology, 2008-08)
Howard, Ayanna M.; Park, Hae Won; Kemp, Charles C.
In this paper, we discuss a methodology to extract play primitives, defined as a sequence of low-level motion behaviors identified during a playing action, such as stacking or inserting a toy. Our premise is that if a robot could interpret the basic movements of a humanpsilas play, it will be able to interact with many different kinds of toys, in conjunction with its human playmate. As such, we present a method that combines motion behavior analysis and behavior sequencing, which capitalizes on the inherent characteristics found in the dynamics of play such as the limited domain of the objects and manipulation skills required. In this paper, we give details on the approach and present results from applying the methodology to a number of play scenarios.
(Georgia Institute of Technology, 2008-06)
Howard, Ayanna M.; Remy, Sekou; Park, Hae Won
Machine learning techniques have currently been deployed in a number of real-world application areas – from casino surveillance to fingerprint matching. That fact, coupled with advances in computer vision and human-computer interfaces, positions systems that can learn from human observation at the point where they can realistically and reliably be deployed as functional components in autonomous control systems. Healthcare applications though pose a unique challenge in that, although autonomous capability might be available, it might not be desired. And yet, based on recent studies focused on assessment of the changing demographics of the world, there is a need for technology that can deal with the shortcomings envisioned in the workforce. Traditional roles for robotics have focused on repetitive, hazardous or dull tasks. If we take the same stance on healthcare applications, we find that some therapeutic activities fall under this traditional classification due to the long-repetitive nature of the therapist-patient interaction. As such, in this paper, we discuss techniques that can be used to model exercise behavior by observing the patient during therapist-patient interaction. The ultimate goal is to monitor patient performance on repetitive exercises, possibly over the course of multiple days between therapy sessions