Hybrid HMM/SVM model for the analysis and segmentation of teleoperation tasks

The automatic execution of a complex task requires the identification of an underlying mental model to derive a possible task control sequence. The model aims at analysing and segmenting the task in simpler sub-tasks. As an example of a complex task, in this paper we consider teleoperation where a person commands a remote robot. This paper presents a new modeling approach using hidden Markov models (HMM) and support vector machines (SVM) to analyse the force/torque signals of a teleoperation task. The task is divided into simpler sub-tasks and the model is used to segment the signals in each sub-task. The segmentation gives informations on the system behavior identifying the changes of the model states. Peg in hole force/torque data are used for testing the model. The results are consistent with the literature with respect to off-line analysis, whereas a significant increase of performance is achieved for on-line analysis.