Multi-dimensional hidden Markov model of telemanipulation tasks with varying outcomes

Three multi-step teleoperation tasks were successfully modeled with a hidden Markov model (HMM). Tasks that would proceed through different paths as determined by an event either internal or external to the task were designed. The tasks can be described by a state transition diagram containing a fork through which two alternative task outcomes can be followed. The model was used to identify correctly the sequence of task progression from the recorded sensor data. Previous work with HMMs was extended by generalizing the model to encompass multi-dimensional sensor signals consisting of a mix of force, torque, and position signals. The addition of multi-dimensional sensor information significantly improved the ability of the Viterbi decoding algorithm to identify the series of events