PDF-A Hand Centric Classification of Human and Robot Dexterous Manipulation

. The user wears an instrumented glove augmented with an armgrounded haptic feedback apparatus A linkage attached to the users wrist measures gross motions of the arm The movements of the user are transferred to a two fingered dexterous robot hand mou Ok am uraNielsSmab and Mark RCutk osky Dexterous ManipulationLaboratory Stanford Univ ersit touchc drstanfor de du Abstract This p ap er for the ICRA 2000 Symp osium on Dex ter ous Manipulation pr esents an overview of ese ar ch in dexter ous manipu sautlisifjussieufr sahbaniccrjussieufr vperdccrjussieufr Abstract This paper addresses the motion planning problem of the dexterous manipulation of 3D rigid objects by a roboti multi64257ngered hand We propose a novel approach based on pro b abilist Dipartimento. . di. . Ingegneria. . dell’Informazione. . Università. . degli. . Studi. . di. Siena . IIT- . Genova. 24 January 2011. A review of Columbia University’s work. Targets. Outline . Mimicking the . Human Hand. April 24, 2013. David French. Types. Prosthetic. Image Courtesy of Luke Skywalker. http://www.scifi-review.net/teenager-gets-a-bionic-hand-that-luke-skywalker-would-envy.html. AB qframecoordinatecontactframecoordinateobject12q Figure1:ypicaldexterousmanipulationproblem:vinganobjectfromcongurationassumedthatismaintainedthroughoutthemanipulation.Therststepindevelopingthekin for image classification. Olga . Russakovsky. , . Yuanqing. Lin,. Kai Yu, Li . Fei-Fei. ECCV 2012. Image classification. Testing:. Does this image contain a car?. Yes. Result. Model. Training:. cars. What is ROBOT ?. DEFINITION. “A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks.”. Hideki . Kozima. and Hiroyuki Yano. Introduction. Shift . from . intentional . stance to design . stance. Attribute ability to the designers not the robot.. I. magine . a robot that has learned and . Atkeson. , CMU. With input from Florian . Jentsch. , UCF . and . Jean Oh, CMU . (Robotics Collaborative Technology Alliance (RCTA), and Katia . Sycara. , CMU. . DARPA Robotics Challenge. DARPA Robotics Challenge: Lessons Learned. . Intuitively obvious approach. …. ANSWERS THE CALL TO ACTION. ALL DM = Hyperglycemia. Classify. each patient by the . specific cause(s. ) of the . β. -cell dysfunction in the clinical presentation of their disease. Brad Pitney. Yin Shi. Chal. . McCollough. Areas of Development. IGA-based . Motion . Generation. Person . Tracking with . Kinect. Right . Arm Kinematics Modeling. IGA-based Motion . Generation. Extends existing music functionality.. Kinematics: . constraints on getting around the environment. The effect of a robot’s geometry on its motion.. kinematics. from sort of simple to sort of difficult. manipulator modeling. wheeled platforms. Overview. Classification Systems. Reasoning and Theory. Treatment Based Classification System. Manipulation. Stabilization. Specific (Directional) Exercise. Traction. Conclusion/Questions. Classification systems.