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Institute for Robotics and Intelligent Machines (IRIM)

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Author: Howard, Ayanna M. × Author: Remy, Sekou × End date: 2009 × Start date: 2000 ×

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Now showing 1 - 8 of 8
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    Improving the performance of ANN training with an unsupervised filtering method
    (Georgia Institute of Technology, 2009-06) Remy, Sekou ; Park, Chung Hyuk ; Howard, Ayanna M.
    Learning control strategies from examples has been identified as an important capability for many robotic systems. In this work we show how the learning process can be aided by autonomously filtering the training set provided to improve key properties of the learning process. Demonstrated with data gathered for manipulation tasks, the results herein show the improved performance when autonomous filtering is applied. The filtration method, with no prior knowledge of the task was able to partition the training sets into sets almost equal to expertly labeled sets. In the case where the filter did not produce the same groupings as the expert user, the method still permitted a controller to be trained which demonstrated a success rate of 92%.
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    3-D Simulations for Testing and Validating Robotic-Driven Applications for Exploring Lunar Pole
    (Georgia Institute of Technology, 2009-04) Williams, Stephen ; Remy, Sekou ; Howard, Ayanna M.
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    Predicting the Robot Learning Curve based on Properties of Human Interaction
    (Georgia Institute of Technology, 2009-03) Remy, Sekou ; Howard, Ayanna M.
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    Quantifying Coherence when Learning Behaviors via Teleoperation
    (Georgia Institute of Technology, 2008-08) Remy, Sekou ; Howard, Ayanna M.
    Applications of robotics are quickly changing. Just as computer use evolved from research purposes to everyday functions, applications of robotics are making a transition to mainstream usage. With this change in applications comes a change in the user base of robotics, and there is a pronounced move to reduce the complexity of robotic control. The move to reduce complexity is linked to the separation of the role of robot designer and robot operator. For many target applications, the operator of the robot needs to be able to correct and augment its capabilities. One method to enable this is learning from human data, which has already been successfully applied to robotics. We assert that this learning process is only viable when the demonstrated human behavior is coherent. In this work we test the hypothesis that quantifying the coherence in the provided instruction can provide useful information about the progress of the learning process. We discuss results from the application of this method to reactive behaviors. Such behaviors permit the learning process to be computationally tractable in real-time. These results support the hypothesis that coherence is important for this type of learning and also show that this property can be used to provide an avenue for self regulation of the learning process.
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    Learning of Arm Exercise Behaviors: Assistive Therapy based on Therapist-Patient Observation
    (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
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    Learning Approaches Applied to Human-Robot Interaction for Space Missions
    (Georgia Institute of Technology, 2008) Remy, Sekou ; Howard, Ayanna M.
    Advances in space science and technology have enabled humanity to reach a stage where we are able to send manned and unmanned vehicles to explore nearby planets. However, given key differences between terrestrial and space environments such as differences in atmospheric content and pressure, acceleration due to gravity among many others between our planet and those we wish to explore, it is not always easy or feasible to expect all mission related tasks to be accomplished by astronauts alone. The presence of robots that specialize in different tasks would greatly enhance our capabilities and enable better overall performance. In this paper we discuss a methodology for building a robotic system that can learn to perform tasks via interactive learning. This learning functionality extends the ability for a robot agent to operate with similar competence as their human teacher- whether astronaut, mission designer, or engineer. We provide details on our approach and give representative examples of applying the different methods in relevant task scenarios.
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    Integrating Virtual and Human Instructors in Robotic Learning Environments
    (Georgia Institute of Technology, 2008) Howard, Ayanna M. ; Remy, Sekou
    This paper presents two different approaches for utilizing virtual environments to enable learning for both human and robotic students. In the first approach, we showcase a 3D interactive environment that allows a human user to learn how to interact with a virtual robot, before interaction with a physical robot. In the second approach, we present a method that utilizes a simulation environment to provide feedback to a human teacher during a training session in order to concurrently allow adaptation of the learning process for both the teacher and the robotic student. We provide details of the approaches in this paper and provide results of the learning outcomes for the two different scenarios.
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    In situ interactive teaching of trustworthy robotic assistants
    (Georgia Institute of Technology, 2007-10) Remy, Sekou ; Howard, Ayanna M.
    In this paper we discuss a method for transferring human knowledge to a robotic platform via teleoperation. The method combines unsupervised clustering and classification with interactive instruction to enable behavior capture in a transferable form. We discuss the approach in both simulation and robotic hardware platform to show the capability of the learning system. In this work we also present a definition and associated metric for trustworthiness, and relate this quantity to system performance. Improved performance and trustworthiness are motivations for our application of interactive learning, and we present results that indicate that these were indeed attained.