Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

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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 15

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.
Robot learners: interactive instance-based learning with social robots

(Georgia Institute of Technology, 2014-04-11) Park, Hae Won

On one hand, academic and industrial researchers have been developing and deploying robots that are used as educational tutors, mediators, and motivational tools. On the other hand, an increasing amount of interest has been placed on non-expert users being able to program robots intuitively, which has led to promising research efforts in the fields of machine learning and human-robot interaction. This dissertation focuses on bridging the gap between the two subfields of robotics to provide personalized experience for the users during educational, entertainment, and therapeutic sessions with social robots. In order to make the interaction continuously engaging, the workspace shared between the user and the robot should provide personalized contexts for interaction while the robot learns to participate in new tasks that arise. This dissertation aims to solve the task-learning problem using an instance-based 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 solutions. The main issues associated with the instance-based approach, i.e., knowledge encoding and acquisition, are addressed in this dissertation research using interactive methods of machine learning. This approach, further referred to as interactive instance-based learning (IIBL), utilizes the keywords people use to convey task knowledge to others to formulate task instances. The key features suggested by the human teacher are extracted during the demonstrations of the task. Regression approaches have been developed in this dissertation to model similarities between cases for instance retrieval including multivariate linear regression and sensitivity analysis using neural networks. The learning performance of the IIBL methods were then evaluated while participants engaged in various block stacking and inserting scenarios and tasks on a touchscreen tablet with a humanoid robot Darwin. In regard to end-users programming robots, the main benefit of the IIBL framework is that the approach fully utilizes the explanatory behavior of the instance-based method which makes the learning process transparent to the human teacher. Such an environment not only encourages the user to produce better demonstrations, but also prompts the user to intervene at the moment a new instance is needed. It was shown through user studies that participants naturally adapt their teaching behavior to the robot learner's progress and adjust the timing and the number of demonstrations. It was also observed that the human-robot teaching and learning scenarios facilitate the emergence of various social behaviors from participants. Encouraging social interaction is often an objective of the task especially with children with cognitive disabilities, and a pilot study with children with autism spectrum disorder revealed promising results comparable to the typically developing group. Finally, this dissertation investigated the necessity of renewable context for prolonged interaction with robot companions. Providing personalized tasks that match each individual's preferences and developmental stages enhances the quality of the user experience with robot learners. Confronted with the limitations of the physical workspace, this research proposes utilizing commercially available touchscreen smart devices as a shared platform for engaging the user in educational, entertainment, and therapeutic tasks with the robot learners. To summarize, this dissertation attempts to defend the thesis statement that a robot learner that utilizes an IIBL approach improves the performance and efficiency of general task learning, and when combined with the state-of-the-art mobile technology that provides personalized context for interaction, enhances the user's experience for prolonged engagement of the task.
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.
Task-Learning Policies for Collaborative Task Solving in Human-Robot Interaction

(Georgia Institute of Technology, 2012) Park, Hae Won

The objective of this doctoral research is to design multimodal task-learning policies for a robotic system that targets the exchange of task rules between humans and robots. This objective is achieved through a collaborative task application during human-robot interaction where the two partners learn a task from each other and accomplish a shared goal. As a first step, a method to model human-action primitives using a pattern-recognition technique is presented. Next, algorithms are developed to generate turn-taking strategies in response to human task behaviors. The contribution of this work is in engaging robots with humans in collaborative play task by modeling statistical patterns of play behaviors and reusing previously learned knowledge to reduce the decision process. Here, results of previous work are presented, and remaining works including deploying a physically embodied agent and developing an evaluation platform are outlined.
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.