Human skill capture: A hidden Markov model of force and torque data in peg-in-a-hole assembly process

A new model has been constructed to generalise the force and torque information during a manual peg-in-a-hole (PiH) assembly process. The paper uses Hidden Markov Model analysis to interpret the state topology (transition probability) and observations (force/torque signal) in the manipulation task. The task can be recognised as several discrete states that reflect the intrinsic nature of the process. Since the whole manipulation process happens so fast, even the operator themselves cannot articulate the exact states. Those are tacit skills which are difficult to extract using human factors methodologies. In order to programme a robot to complete tasks at skill level, numerical representation of the sub-goals are necessary. Therefore, those recognised ‘hidden’ states become valuable when a detail explanation of the task is needed and when a robot controller needs to change its behaviour in different states. The Gaussian Mixture model (GMM) is used as the initial guess of observations distribution. Then a Hidden Markov Model is used to encode the state (sub-goal) topology and observation density associated with those sub-goals. The Viterbi algorithm is then applied for the model-based analysis of the force and torque signal and the classification into sub-goals. The Baum-Welch algorithm is used for training and to estimate the most likely model parameters. In addition to generic states recognition, the proposed method also enhances our understanding of the skill based performances in manual tasks.