Grasp Recognition for Programming by Demonstration

The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved. The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased.

[1]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[2]  H. Bülthoff,et al.  Merging the senses into a robust percept , 2004, Trends in Cognitive Sciences.

[3]  Monica N. Nicolescu,et al.  Natural methods for robot task learning: instructive demonstrations, generalization and practice , 2003, AAMAS '03.

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

[5]  Danica Kragic,et al.  Interactive grasp learning based on human demonstration , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[6]  Simon Ferguson,et al.  Grasp Recognition From Myoelectric Signals , 2002 .

[7]  Stefano Caselli,et al.  Leveraging on a virtual environment for robot programming by demonstration , 2004, Robotics Auton. Syst..

[8]  Katsushi Ikeuchi,et al.  Toward automatic robot instruction from perception-recognizing a grasp from observation , 1993, IEEE Trans. Robotics Autom..

[9]  Rüdiger Dillmann,et al.  Interactive Robot Programming Based on Human Demonstration and Advice , 1998, Sensor Based Intelligent Robots.

[10]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[11]  Katsushi Ikeuchi,et al.  A Hidden Markov Model Based Sensor Fusion Approach for Recognizing Continuous Human Grasping Sequences , 2003 .