Now showing items 779-798 of 1575

    • Learning about Objects with Human Teachers 

      Thomaz, Andrea L.; Cakmak, Maya (Georgia Institute of TechnologyAssociation for Computing Machinery, 2009)
      A general learning task for a robot in a new environment is to learn about objects and what actions/effects they afford. To approach this, we look at ways that a human partner can intuitively help the robot learn, ...
    • Learning and Inference in Parametric Switching Linear Dynamic Systems 

      Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2005-10)
      We introduce parametric switching linear dynamic systems (P-SLDS) for learning and interpretation of parametrized motion, i.e., motion that exhibits systematic temporal and spatial variations. Our motivating example is ...
    • Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems 

      Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank (Georgia Institute of Technology, 2006)
      Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM by using continuous hidden states. However, ...
    • Learning and Inferring Motion Patterns Using Parametric Segmental Switching Linear Dynamic Systems 

      Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank (Georgia Institute of TechnologySpringer Berlin / Heidelberg, 2008)
      Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS provides the possibility to describe complex temporal patterns more concisely and accurately ...
    • A Learning Approach to Enable Locomotion of Multiple Robotic Agents Operating in Natural Terrain Environments 

      Howard, Ayanna; Parker, Lonnie T.; Smith, Brian Stephen (Georgia Institute of TechnologyTSI press, 2008)
      This paper presents a methodology that utilizes soft computing approaches to enable locomotion of multiple legged robotic agents operating in natural terrain environments. For individual robotic control, the locomotion ...
    • Learning Approaches Applied to Human-Robot Interaction for Space Missions 

      Remy, Sekou; Howard, Ayanna (Georgia Institute of TechnologyTSI press, 2008)
      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 ...
    • Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning 

      Arkin, Ronald C.; Kaess, Michael; Likhachev, Maxim (Georgia Institute of Technology, 2001)
      This paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It is based on our previous work on a behavior-based ...
    • Learning Contact Locations for Pushing and Orienting Unknown Objects 

      Hermans, Tucker; Li, Fuxin; Rehg, James M.; Bobick, Aaron F. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2013-10)
      We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records ...
    • Learning descriptive models of objects and activities from egocentric video 

      Fathi, Alireza (Georgia Institute of Technology, 2013-06-13)
      Recent advances in camera technology have made it possible to build a comfortable, wearable system which can capture the scene in front of the user throughout the day. Products based on this technology, such as GoPro and ...
    • Learning from Examples in Unstructured, Outdoor Environments 

      Sun, J.; Mehta, Tejas R.; Wooden, David; Powers, Matthew; Rehg, J.; Balch, Tucker; Egerstedt, Magnus B. (Georgia Institute of TechnologyWiley Periodicals, Inc., 2006)
      In this paper, we present a multi-pronged approach to the "Learning from Example" problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both ...
    • Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies 

      Roberts, Richard; Potthast, Christian; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2009)
      This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging ...
    • A Learning Methodology for Robotic Manipulation of Deformable Objects 

      Howard, Ayanna M.; Bekey, George A. (Georgia Institute of Technology, 2000-06)
      The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper address the problem of robotic grasping and manipulation of 3- D ...
    • Learning Momentum: Integration and Experimentation 

      Arkin, Ronald C.; Lee, J. Brian (Georgia Institute of Technology, 2000)
      We further study the effects of learning momentum as defined by Clark, Arkin, and Ram[1] on robots, both simulated and real, attempting to traverse obstacle fields in order to reach a goal. Integration of these results ...
    • Learning Momentum: On-Line Performance Enhancement for Reactive Systems 

      Arkin, Ronald C.; Clark, Russell J.; Ram, Ashwin (Georgia Institute of Technology, 1992)
      We describe a reactive robotic control system which incorporates aspects of machine learning to improve the system's ability to successfully navigate in unfamiliar environments. This system overcomes limitations of completely ...
    • Learning Multi-Modal Control Programs 

      Mehta, Tejas R.; Egerstedt, Magnus B. (Georgia Institute of TechnologySpringer-Verlag, 2005-03)
      Multi-modal control is a commonly used design tool for breaking up complex control tasks into sequences of simpler tasks. In this paper, we show that by viewing the control space as a set of such tokenized instructions ...
    • Learning Object Models for Humanoid Manipulation 

      Stilman, Mike; Nishiwaki, Koichi; Kagami, Satoshi (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2007-11)
      We present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. ...
    • Learning of Arm Exercise Behaviors: Assistive Therapy based on Therapist-Patient Observation 

      Howard, Ayanna M.; Remy, Sekou; Park, Hae Won (Georgia Institute of Technology, 2008-06)
      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 ...
    • Learning of Parameter-Adaptive Reactive Controllers for Robotic Navigation 

      Ramesh, Ashwin; Santamaria, Juan Carlos (Georgia Institute of Technology, 1997)
      Reactive controllers are widely used in mobile robots because they are able to achieve successful performance in real-time. However, the configuration of a reactive controller depends highly on the operating conditions of ...
    • The Learning of Reactive Control Parameters Through Genetic Algorithms 

      Arkin, Ronald C.; Pearce, Michael; Ram, Ashwin (Georgia Institute of Technology, 1992)
      This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach is to train a reactive control system in various types of environments, ...
    • Learning Sparse Covariance Patterns for Natural Scenes 

      Wang, Liwei; Li, Yin; Jia, Jiaya; Sun, Jian; Wipf, David; Rehg, James M. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2012-06)
      For scene classification, patch-level linear features do not always work as well as hand-crafted features. In this paper, we present a new model to greatly improve the discrimination power of linear features in ...