Experience based Learning and Control of Robotic Grasping

Johan Tegin and Jan Wikander Mechatronics Laboratory Machine Design KTH, Stockholm, Sweden Email: johant, jan@md.kth.se Staffan Ekvall and Danica Kragic Computational Vision and Active Perception Laboratory KTH, Stockholm, Sweden Email: ekvall, danik@nada.kth.se Boyko Iliev Biologically Inspired Systems Laboratory Applied Autonomous Sensor Systems Örebro University, Örebro, Sweden Email: boyko.iliev@tech.oru.se

[1]  J. Napier The prehensile movements of the human hand. , 1956, The Journal of bone and joint surgery. British volume.

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

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

[4]  Peter K. Allen,et al.  GraspIt!: A Versatile Simulator for Grasp Analysis , 2000, Dynamic Systems and Control: Volume 2.

[5]  Danica Kragic,et al.  Receptive field cooccurrence histograms for object detection , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Danica Kragic,et al.  Grasp Recognition for Programming by Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  Danica Kragic,et al.  Object recognition and pose estimation using color cooccurrence histograms and geometric modeling , 2005, Image Vis. Comput..

[8]  Jan Wikander,et al.  A FRAMEWORK FOR GRASP SIMULATION AND CONTROL IN DOMESTIC ENVIRONMENTS , 2006 .

[9]  Dirk Kraft,et al.  An anthropomorphic grasping approach for an assistant humanoid robot , 2007 .