Representation and mapping of dexterous manipulation through task primitives

The goal of this work is to teach a robot to regrasp an object using knowledge obtained from human demonstration. This paper presents a task model that represents a human regrasping movement. The task model is based on the topological information and comprised of four task primitives. Human regrasping movement is recognised and represented as a sequence of these task primitives by the proposed recognition algorithm. The proposed method then maps each task primitive to the target robot hand using knowledge obtained from human demonstration. The experimental result verified the proposed task model by executing the regrasping movement on the real robot hand.

[1]  Danica Kragic,et al.  Grasp recognition and mapping on humanoid robots , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[2]  Miriam Zacksenhouse,et al.  Coordinative Structure of Manipulative Hand-Movements Facilitates Their Recognition , 2006, IEEE Transactions on Biomedical Engineering.

[3]  Peter K. Allen,et al.  Graspit! A versatile simulator for robotic grasping , 2004, IEEE Robotics & Automation Magazine.

[4]  Helge J. Ritter,et al.  Towards dextrous manipulation using manipulation manifolds , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Masayuki Inaba,et al.  Learning by watching: extracting reusable task knowledge from visual observation of human performance , 1994, IEEE Trans. Robotics Autom..

[6]  Katsushi Ikeuchi,et al.  Toward an assembly plan from observation. I. Task recognition with polyhedral objects , 1994, IEEE Trans. Robotics Autom..

[7]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[8]  Katsushi Ikeuchi,et al.  Toward automatic robot instruction from perception-mapping human grasps to manipulator grasps , 1997, IEEE Trans. Robotics Autom..

[9]  Tsukasa Ogasawara,et al.  Recognition of in-hand manipulation using contact state transition for multifingered robot hand control , 2008, Robotics Auton. Syst..

[10]  Taku Komura,et al.  Character Motion Synthesis by Topology Coordinates , 2009, Comput. Graph. Forum.

[11]  Thea Iberall,et al.  Human Prehension and Dexterous Robot Hands , 1997, Int. J. Robotics Res..

[12]  K. Klenin,et al.  Computation of writhe in modeling of supercoiled DNA. , 2000, Biopolymers.

[13]  Miriam Zacksenhouse,et al.  Inherent structure of manipulative hand movements and its discriminative power , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[14]  J. Elliott,et al.  A CLASSIFICATION OF MANIPULATIVE HAND MOVEMENTS , 1984, Developmental medicine and child neurology.

[15]  Taku Komura,et al.  Indexing and Retrieving Motions of Characters in Close Contact , 2009, IEEE Transactions on Visualization and Computer Graphics.

[16]  S. Buss.  Introduction to Inverse Kinematics with Jacobian Transpose , Pseudoinverse and Damped Least Squares methods , 2004 .

[17]  Helge J. Ritter,et al.  Structured unsupervised kernel regression for closed-loop motion control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Phongtharin Vinayavekhin,et al.  Towards an automatic robot regrasping movement based on human demonstration using tangle topology , 2011, 2011 IEEE International Conference on Robotics and Automation.