  Uncertainty is a major difficulty in endowing robots with autonomy. Robots
often fail due to unexpected events. In robot contact tasks are often design to
empirically look for force thresholds to define state transitions in a Markov
chain or finite state machines. Such design is prone to failure in unstructured
environments, when due to external disturbances or erroneous models, such
thresholds are met, and lead to state transitions that are false-positives. The
focus of this paper is to perform high-level state estimation of robot
behaviors and task output for robot contact tasks. Our approach encodes raw
low-level 3D cartesian trajectories and converts them into a high level (HL)
action grammars. Cartesian trajectories can be segmented and encoded in a way
that their dynamic properties, or "texture" are preserved. Once an action
grammar is generated, a classifier is trained to detect current behaviors and
ultimately the task output. The system executed HL state estimation for task
output verification with an accuracy of 86%, and behavior monitoring with an
average accuracy of: 72%. The significance of the work is the transformation of
difficult-to-use raw low-level data to HL data that enables robust behavior and
task monitoring. Monitoring is useful for failure correction or other
deliberation in high-level planning, programming by demonstration, and
human-robot interaction to name a few.
