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Abstract

Force signals provide essential information for manipulation. This thesis focuses on monitoring the contact state of a robot arm (or a haptic device) with the environment. We describe a procedure to segment and interpret force signals by using a statistical, model-based approach. This idea will be useful for high level robot programming, as force signals are not compatible between different devices (robot arm, or haptic device), and costly to transmit. To relate the force data stream to the parameters of interest, we address the criteria of dividing tasks into subtasks by detecting the changes of the observations based on a specific force signal input device, each of the subtasks corresponding to. an auto-regression model. Each hypothesized contact model has an estimator. The observations of position, velocity, and force are input into a collection of estimators. The estimators output the measure of match as well as the residual process to be fed back to the state change detector. So we can detect a subtask and select a model from a set of candidate models to determine the state of contact. In this thesis, we simplify and improve the traditional approach of change detection and estimation to make it suitable for manipulation tasks. The context is also a fundamental to manipulation. The sequence of subtasks determines the task structure, and thus the goal of the operator. The Markov process encodes the subtasks and prior knowledge with each subtask state.