Organizational Unit:
Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Includes Organization(s)
Organizational Unit
Organizational Unit
ArchiveSpace Name Record
7 results
Publication Search Results
Now showing
1 - 7 of 7
-
ItemKeyframe-based Learning from Demonstration Method and Evaluation(Georgia Institute of Technology, 2012-06) Akgun, Baris ; Cakmak, Maya ; Jiang, Karl ; Thomaz, Andrea L.We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.
-
ItemMulti-Cue Contingency Detection(Georgia Institute of Technology, 2012-04) Lee, Jinhan ; Chao, Crystal ; Bobick, Aaron F. ; Thomaz, Andrea L.The ability to detect a human's contingent response is an essential skill for a social robot attempting to engage new interaction partners or maintain ongoing turn-taking interactions. Prior work on contingency detection focuses on single cues from isolated channels, such as changes in gaze, motion, or sound.We propose a framework that integrates multiple cues for detecting contingency from multimodal sensor data in human-robot interaction scenarios. We describe three levels of integration and discuss our method for performing sensor fusion at each of these levels. We perform a Wizard-of-Oz data collection experiment in a turn-taking scenario in which our humanoid robot plays the turn-taking imitation game “Simon says" with human partners. Using this data set, which includes motion and body pose cues from a depth and color image and audio cues from a microphone, we evaluate our contingency detection module with the proposed integration mechanisms and show gains in accuracy of our multi-cue approach over single-cue contingency detection. We show the importance of selecting the appropriate level of cue integration as well as the implications of varying the referent event parameter.
-
ItemAutomated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid and Local Binary Patterns in Texture and Shape Encoding(Georgia Institute of Technology, 2011-10) Liu, Yu-Ying ; Chen, Mei ; Ishikawa, Hiroshi ; Wollstein, Gadi ; Schuman, Joel S. ; Rehg, James M.We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC > 0:93).
-
ItemApproximate Reasoning for Safety and Survivability of Planetary Rovers(Georgia Institute of Technology, 2003-02) Tunstel, Edward ; Howard, Ayanna M.Operational safety and health monitoring are critical matters for autonomous planetary rovers operating on remote and challenging terrain. This paper describes rover safety issues and presents an approximate reasoning approach to maintaining vehicle safety in a navigational context. The proposed rover safety module is composed of two distinct behaviors: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic implementations of these behaviors on outdoor terrain is presented. Sensing of vehicle safety coupled with visual neural network-based perception of terrain quality are used to infer safe speeds during rover traversal. In addition, approximate reasoning for self-regulation of internal operating conditions is briefly discussed. The core theoretical foundations of the applied soft computing techniques is presented and supported by descriptions of field tests and laboratory experimental results. For autonomous rovers, the approach provides intrinsic safety cognizance and a capacity for reactive mitigation of navigation risks.
-
ItemRule-based reasoning and neural network perception for safe off-road robot mobility(Georgia Institute of Technology, 2002-09) Tunstel, Edward ; Howard, Ayanna M. ; Seraji, HomayounOperational safety and health monitoring are critical matters for autonomous field mobile robots such as planetary rovers operating on challenging terrain. This paper describes relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a mobility and navigation context. The proposed rover safety module is composed of two distinct components: safe attitude (pitch and roll) management and safe traction management. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented, wherein inertial sensing of safety status and vision-based neural network perception of terrain quality are used to infer safe speeds of traversal. Results of initial field tests and laboratory experiments are also described. The approach provides an intrinsic safety cognizance and a capacity for reactive mitigation of robot mobility and navigation risks.
-
ItemA generalized approach to real-time pattern recognition in sensed data(Georgia Institute of Technology, 1999-12) Howard, Ayanna M. ; Padgett, CurtisMany applications that focus on target detection in an image scene develop algorithms specific to the task at hand. These algorithms tend to be dependent on the type of input data used in the application and thus generally fail when transplanted to other detection spaces. We wish to address this data dependency issue and develop a novel technique which autonomously detects, in real time, all target objects embedded in an image scene irrespective of the imagery representation. We accomplish this task using a heirarchical approach in which we use an optimal set of linear filters to reduce the data dimensionality of an image scene and then spatially locate objects in the scene with a neural network classifier. We prove the generality of this approach by applying it to two distinctly separate applications. In the first application, we use our algorithm to detect a specified set of targets for an Automatic Target Recognition (ATR) task. The data for this application is retrieved from two-dimensional camera imagery. In the second task, we address the problem of sub-pixel target detection in a hyperspectral image scene. This data set is represented by hyperspectral pixel bands in which target objects occupy a portion of a hyperspectral pixel. A summarized description of our algorithm is given in the following section.
-
ItemModeling Mechanisms with Nonholonomic Joints Using the Boltzmann-Hamel Equations(Georgia Institute of Technology, 1997-02) Obergfell, Klaus ; Book, Wayne J.This article describes a new technique for deriving dynamic equations of motion for serial chain and tree topology mech anisms with common nonholonomic constraints. For each type of nonholonomic constraint, the Boltzmann-Hamel equations produce a concise set of dynamic equations. These equations are similar to Lagrange's equations and can be applied to mechanisms that incorporate that type of constraint. A small library of these equations can be used to efficiently analyze many different types of mechanisms. Nonholonomic constraints are usually included in a La grangian setting by adding Lagrange multipliers and then eliminating them from the final set of equations. The ap proach described in this article automatically produces a minimum set of equations of motion that do not include La grange multipliers.