Robust trajectory segmentation for programming by demonstration

A novel trajectory segmentation and modeling approach is presented. Trajectory segmentation and matching is an important step in the programming by demonstration (PbD) process to extract the user's intentions from multiple trajectories. To match multiple trajectories, the segmentation and modeling approach must be consistent and robust to disparities caused by robot dynamics and human imperfections. Several curve segmentation approaches have demonstrated substantial potential in the field of image processing and gesture recognition. They emphasize reduction of the degree of mismatch between given and model curves. However they fail to reduce mismatch between models of multiple trajectories recorded to demonstrate the same intention.We propose an M-estimator for trajectory modeling and set up a new segmentation criterion to address the issue. The proposed approach is better suited for PbD of mobile robots. The approach is evaluated for real robot trajectories.

[1]  Danica Kragic,et al.  Learning Task Models from Multiple Human Demonstrations , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[2]  Philip S. Yu,et al.  Global distance-based segmentation of trajectories , 2006, KDD '06.

[3]  Cédric Hartland,et al.  Using echo state networks for robot navigation behavior acquisition , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[4]  Chengcui Zhang,et al.  Interactive mining and semantic retrieval of videos , 2007, MDM '07.

[5]  Katsushi Ikeuchi,et al.  A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models , 2005, IEEE Transactions on Robotics.

[6]  Andrew W. Fitzgibbon,et al.  Stable segmentation of 2D curves , 1998 .

[7]  D. Lane,et al.  Superellipse Fitting for the Recovery and Classification of Mine-Like Shapes in Sidescan Sonar Images , 2008, IEEE Journal of Oceanic Engineering.

[8]  Richard Mann,et al.  Categorization and learning of pen motion using hidden Markov models , 2004, First Canadian Conference on Computer and Robot Vision, 2004. Proceedings..

[9]  Rüdiger Dillmann,et al.  Understanding users intention: programming fine manipulation tasks by demonstration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Rüdiger Dillmann,et al.  Learning sequential constraints of tasks from user demonstrations , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[11]  Bruce A. MacDonald,et al.  An intuitive interface for a cognitive programming by demonstration system , 2008, 2008 IEEE International Conference on Robotics and Automation.

[12]  Jun Zhang,et al.  Glomerulus Extraction by Optimizing the Fitting Curve , 2008, 2008 International Symposium on Computational Intelligence and Design.

[13]  Narendra Ahuja,et al.  Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Mubarak Shah,et al.  Motion trajectories , 1993, IEEE Trans. Syst. Man Cybern..

[15]  Nasser Sherkat,et al.  Automated path segmentation for 2-dimensional vectorised data , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[16]  Richard Mann,et al.  Detecting Hand-Ball Events in Video Sequences , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[17]  Allan D. Jepson,et al.  Detection and classification of motion boundaries , 2002, AAAI/IAAI.

[18]  Xiao-Shan Gao,et al.  Rational quadratic approximation to real plane algebraic curves , 2004, Geometric Modeling and Processing, 2004. Proceedings.

[19]  Stephen A. Billings,et al.  Robot programming by demonstration through system identification , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Rainer Palm,et al.  Segmentation and Recognition of Human Grasps for Programming-by-Demonstration using Time-clustering and Fuzzy Modeling , 2007, 2007 IEEE International Fuzzy Systems Conference.