Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 13

Retrieving Experience: Interactive Instance-based Learning Methods for Building Robot Companions

(Georgia Institute of Technology, 2015-05) Park, Hae Won ; Howard, Ayanna M.

A robot companion should adapt to its user’s needs by learning to perform new tasks. In this paper, we present a robot playmate that learns and adapts to tasks chosen by the child on a touchscreen tablet. We aim to solve the task learning problem using an experience-based learning framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predictions of task behaviors based on prior actions. In order to automate the processes of instance encoding, acquisition, and retrieval, we have developed a framework that gathers task knowledge through interaction with human teachers. This approach, further referred to as interactive instance-based learning (IIBL), utilizes limited information available to the robot to generate similarity metrics for retrieving instances. In this paper, we focus on introducing and evaluating a new hybrid IIBL framework using sensitivity analysis with artificial neural networks and discuss its advantage over methods using k-NNs and linear regression in retrieving instances.
Robot Learners: Interactive Instance-based Learning and Its Application to Therapeutic Tasks

(Georgia Institute of Technology, 2014-11) Park, Hae Won ; Howard, Ayanna M.

Programming a robot to perform tasks requires training that is beyond the skill level of most individuals. To address this issue, we focus on developing a method that identifies keywords used to convey task knowledge among people and a framework that uses these keywords as conditions for knowledge acquisition by the robot learner. The methodology includes generalizing task modeling and providing a robot learner the ability to learn and improve its skills through accumulated experience gained from interaction with humans. More specifically, the aim of this research addresses the issues of knowledge encoding, acquisition, and retrieval through interactive instance-based learning (IIBL). In interaction studies, the benefit of using such a robot learner is in promoting social behaviors that results from the participant taking on an active role as teacher. Our recent experiment with 33 participants, including 19 typically developing children, and a pilot study with two children with autism spectrum disorder showed that IIBL provides a framework for designing an effective robot learner, and that the robot learner successfully increases the amount of social interactions initiated by the participants.
Using a Shared Tablet Workspace for Interactive Demonstrations during Human-Robot Learning Scenarios

(Georgia Institute of Technology, 2014) Park, Hae Won ; Coogle, Richard A. ; Howard, Ayanna M.

One of the key elements for building a long-term robotic companion is incorporating the ability for a robot to continuously learn and engage in new tasks. Utilizing a defined workspace that provides various shared content between human and robot could assist in this learning process. Here, we propose integrating a touchscreen tablet and a robot learner for engaging the user during human-robot interaction scenarios. The robot learner’s domain-independent core reasoner follows the structure of instance-based learning which addresses the issues of acquiring knowledge, encoding cases, and learning a retrieval metric. The system utilizes demonstrations provided by the user to auto-populate the knowledge base through natural interaction methods, encodes cases based on the feature structure provided by the user, and uses an adaptive-weighting technique to design a retrieval metric with linear regression in the feature-distance space. Through a tablet environment, the user teaches a task to the robot in a shared workspace and intuitively monitors the robot’s behavior and progress in real time. In this setting, the user is able to interrupt the robot and provide necessary demonstrations at the moment learning is taking place, thus providing a means to continuously engage both the participant and the robot in the learning cycle.
Engaging Children In Social Behavior: Interaction With a Robot Playmate Through Tablet-based Apps

(Georgia Institute of Technology, 2014) Park, Hae Won ; Howard, Ayanna M.

There has been an emerging use of touchscreen-based smart devices, such as the iPad, for assisting in education and communication interventions for children with Autism Spectrum Disorder (ASD). There has also been growing evidence of the utilization of robots to foster social interaction in children with ASD. Unfortunately, although interventions using the tablet have been successfully implemented in the home environment, the robotic platforms have not. One of the reasons is due to the fact that these robotic platforms are typically not autonomous, i.e. they are typically controlled directly by the clinician or through pre-scripted behavior. This makes it difficult for immersion of such platforms in an environment outside of the clinical setting. As such, to capitalize on the widespread ease-of-use of tablet devices and the emerging success found in the field of social robotics, we present efforts that focus on designing an autonomous interactive robot that socially interacts with a child using the tablet as a shared medium. The purpose is to foster social interaction through play that is directed by the child, thus moving toward behavior that can be translated outside of the clinical setting.
Engaging Children in Play Therapy: The Coupling of Virtual Reality (VR) Games With Social Robotics

(Georgia Institute of Technology, 2014-01) García-Vergara, Sergio ; Brown, LaVonda ; Park, Hae Won ; Howard, Ayanna M.

Individuals who have impairments in their motor skills typically engage in rehabilitation protocols to improve the recovery of their motor functions. In general, engaging in physical therapy can be tedious and difficult, which can result in demotivating the individual. This is especially true for children who are more susceptible to frustration. Thus, different virtual reality environments and play therapy systems have been developed with the goal of increasing the motivation of individuals engaged in physical therapy. However, although previously developed systems have proven to be effective for the general population, the majority of these systems are not focused on engaging children. Given this motivation, we discuss two technologies that have been shown to positively engage children who are undergoing physical therapy. The first is called the Super Pop VR™ game; a virtual reality environment that not only increases the child’s motivation to continue with his/her therapy exercises, but also provides feedback and tracking of patient performance during game play. The second technology integrates robotics into the virtual gaming scenario through social engagement in order to further maintain the child’s attention when engaged with the system. Results from preliminary studies with typically-developing children have shown their effectiveness. In this chapter, we discuss the functions and advantages of these technologies, and their potential for being integrated into the child’s intervention protocol.
Providing tablets as collaborative task workspace for human-robot interaction

(Georgia Institute of Technology, 2013-03) Park, Hae Won ; Howard, Ayanna M.

In a recent conference on assistive technology in special education and rehabilitation, over 54 percentage of the sessions were directly or indirectly involved with tablets. Following this trend, many traditional assistive technologies are now transitioning from standalone devices into apps on mobile devices. As such, this paper follows this trend by discussing transforming a tablet into an HRI research platform where our robotic system engages the user in social interaction by learning how to operate a given app (task) using guidance from the user. The objective is to engage the robot within the context of the user's task by understanding the task's underlying rules and structures. An overview of the HRI toolkit is presented and a knowledge-based approach in modeling a task is discussed where previously learned cases are reused to solve a new problem.
TabAccess, a Wireless Controller for Tablet Accessibility for Individuals with Limited Upper-Body Mobility

(Georgia Institute of Technology, 2013-02) Park, Hae Won ; Howard, Ayanna M.

Over 3 million individuals in the US have a disability in their hands and/or forearms and thus have difficulties in effecting pinch and swipe gestures needed for tablet interaction. In this paper, a forearm mountable mobile interface, TabAccess (controller for Tablet Accessibility) is introduced. The objective is to provide an input interface for individuals with limited manipulation skills an alternative way to interact with touchscreen tablet applications. We believe that by combining TabAccess with mobile computers, effective education and entertainment opportunities could be delivered to persons lacking fine motor skills. For translation of gross motor gestures into touchscreen-based gestures, a methodology was developed to convert raw sensor data retrieved from the sensors into press and swipe gestures. The proposed device recognizes different gestures generated by a combination of sensors with hidden Markov models. This paper presents the design specifications of TabAccess, and discusses the training and testing results with three diverse applications - a music player, a robot controller, and a communication app.
Robots and Therapeutic Play: Evaluation of a Wireless Interface Device for Interaction with a Robot Playmate

(Georgia Institute of Technology, 2012) Roberts, Luke ; Park, Hae Won ; Howard, Ayanna M.

Rehabilitation robots in home environments has the potential to dramatically improve quality of life for individuals who experience disabling circumstances due to injury or chronic health conditions. Unfortunately, although classes of robotic systems for rehabilitation exist, these devices are typically not designed for children. And since over 150 million children in the world live with a disability, this causes a unique challenge for deploying such robotics for this target demographic. To overcome this barrier, we discuss a system that uses a wearable arm glove input device to enable interaction with a robotic playmate during various play scenarios. Results from testing the system with 20 human subjects show that the system has potential, and a user specific device calibration algorithm is proposed to improve the performance of the system.
Case-Based Reasoning for Planning Turn-Taking Strategy with a Therapeutic Robot Playmate

(Georgia Institute of Technology, 2010-09) Park, Hae Won ; Howard, Ayanna M.

In this paper, we focus on robot intelligence to generate turn-taking strategies in response to human play actions. This work builds on our previous work on play behavior recognition, and expands it to the child-robot therapeutic domain where the robot must understand and learn the play of a child and take turns manipulating the toys. The main contribution of this work is a novel attempt in applying Case-Based Reasoning (CBR) for planning human-robot turn-taking strategies. By comparing the child's play in the current scene to some past play cases stored in memory, we retrieve the best solution and adapt it to the set of toys that are available for the play scenario, bypassing a long complicated decision process. In order to ensure real-time performance, a low dimension scale invariant shape descriptor is proposed for shape matching. Turn-taking CBR (ttCBR) system is then evaluated for stacking and inserting tasks with four subjects, by comparing the decision made by the system and the actual choice of the humans.
Understanding a Child’s play for Robot Interaction by Sequencing Play Primitives using Hidden Markov Models

(Georgia Institute of Technology, 2010-05) Park, Hae Won ; Howard, Ayanna M.

In this paper, we discuss a methodology to build a system for a robot playmate that extracts and sequences low-level play primitives during a robot-child interaction scenario. The motivation is to provide a robot with basic knowledge of how to manipulate toys in an equivalent manner as a human does - as a first step in engaging children in cooperative play. Our approach involves the extraction of play primitives based on observation of motion gradient vectors computed from the image sequence. Hidden Markov Models (HMMs) are then used to recognize 14 different play primitives during play. Experimental results from a data set of 100 play scenarios including child subjects demonstrate 86.88% accuracy recognizing and sequencing the play primitives.