Now showing items 794-813 of 1591

    • 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 ...
    • Learning Stable Pushing Locations 

      Hermans, Tucker; Li, Fuxin; Rehg, James M.; Bobick, Aaron F. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2013-08)
      We present a method by which a robot learns to predict effective push-locations as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and ...
    • Learning task performance in market-based task allocation 

      Pippin, Charles, E.; Christensen, Henrik I. (Georgia Institute of TechnologySpringer-Verlag, 2012-06)
      Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, ...
    • Learning to Locomote: Action Sequences and Switching Boundaries 

      O'Flaherty, Rowland; Egerstedt, Magnus B. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2013-08)
      This paper presents a hybrid control strategy for learning the switching boundaries between primitive controllers that maximize the translational movements of complex locomoting systems. Through this abstraction, the ...
    • Learning to Recognize Daily Actions using Gaze 

      Fathi, Alireza; Li, Yin; Rehg, James M. (Georgia Institute of Technology, 2012-10)
      We present a probabilistic generative model for simultaneously recognizing daily actions and predicting gaze locations in videos recorded from an egocentric camera. We focus on activities requiring eye-hand coordination ...
    • Learning to Recognize Objects in Egocentric Activities 

      Fathi, Alireza; Ren, Xiaofeng; Rehg, James M. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2011-06)
      This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know only the names of the objects which are ...
    • Learning to Role-Switch in Multi-Robot Systems 

      Arkin, Ronald C.; Martinson, Eric (Georgia Institute of Technology, 2003)
      We present an approach that uses Q-learning on individual robotic agents, for coordinating a mission-tasked team of robots in a complex scenario. To reduce the size of the state space, actions are grouped into sets ...
    • Learning Visibility of Landmarks for Vision-Based Localization 

      Alcantarilla, Pablo F.; Oh, Sang Min; Mariottini, Gian Luca; Bergasa, Luis M.; Dellaert, Frank (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2010)
      We aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from the image with the map elements at the current ...
    • Lek Behavior as a Model for Multi-Robot Systems 

      Duncan, Brittany A.; Ulam, Patrick D.; Arkin, Ronald C. (Georgia Institute of Technology, 2009-01-01)
      Lek behavior is a biological mechanism used by male birds to attract mates by forming a group. This project explores the use of a biological behavior found in many species of birds to form leks to guide the creation of ...
    • Less Is More: Mixed Initiative Model Predictive Control with Human Inputs 

      Chipalkatty, Rahul; Droge, Greg; Egerstedt, Magnus B. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2013-06)
      This paper presents a new method for injecting human inputs into mixed-initiative interactions between humans and robots. The method is based on a model-predictive control (MPC) formulation, which inevitably involves ...