Layered HMM for Motion Intention Recognition

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers

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

[2]  Blake Hannaford,et al.  Multi-dimensional hidden Markov model of telemanipulation tasks with varying outcomes , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[3]  Samy Bengio,et al.  Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[4]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[5]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[6]  Gregory D. Hager,et al.  Human-Machine Collaborative Systems for Microsurgical Applications , 2005, Int. J. Robotics Res..

[7]  Paolo Fiorini,et al.  Hybrid HMM/SVM model for the analysis and segmentation of teleoperation tasks , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Allison M. Okamura,et al.  Recognition of operator motions for real-time assistance using virtual fixtures , 2003, 11th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2003. HAPTICS 2003. Proceedings..

[9]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[10]  Rüdiger Dillmann,et al.  Building elementary robot skills from human demonstration , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[11]  Steve Renals,et al.  Dynamic Bayesian networks for meeting structuring , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Shih-Fu Chang,et al.  Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[13]  J. Edward Colgate,et al.  Cobot architecture , 2001, IEEE Trans. Robotics Autom..

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

[15]  Ming Ouhyoung,et al.  A real-time continuous gesture recognition system for sign language , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[16]  Danica Kragic,et al.  Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Gregory D. Hager,et al.  Building a task language for segmentation and recognition of user input to cooperative manipulation systems , 2002, Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002.

[18]  Redwan Alqasemi,et al.  Telemanipulation Assistance Based on Motion Intention Recognition , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[19]  Russell H. Taylor,et al.  Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..

[20]  Gerhard Rigoll,et al.  Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).