Robot Contact Task State Estimation via Action Grammars

TL;DR

This paper introduces a method to improve robot contact task state estimation by converting raw 3D trajectories into high-level action grammars, enabling more robust behavior detection and task verification in unstructured environments.

Contribution

It presents a novel approach to encode low-level robot trajectories into high-level action grammars for accurate behavior and task state estimation.

Findings

High-level state estimation accuracy of 86% for task output.

Behavior monitoring achieved an average accuracy of 72%.

Transforms raw trajectory data into robust high-level representations.

Abstract

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.