Open Collections

Real-time predictions from unlabeled high-dimensional sensory data during non-prehensile manipulation

University of British Columbia

Robots can be readily equipped with sensors that span a growing range of modalities and price-points. However, as sensors increase in number and variety, making the best use of the rich multi-modal sensory streams becomes increasingly challenging. In this thesis, we demonstrate the ability to make efficient and accurate task-relevant predictions from unlabeled streams of sensory data for a non-prehensile manipulation task. Specifically, we address the problem of making real-time predictions of the mass, friction coefficient, and compliance of a block during a topple-slide task, using an unlabeled mix of 1650 features composed of pose, velocity, force, torque, and tactile sensor data samples taken during the motion. Our framework employs a partial least squares (PLS) estimator as computed based on training data. Importantly, we show that the PLS predictions can be made significantly more accurate and robust to noise with the use of a feature selection heuristic, the task variance ratio, while using as few as 5% of the original sensory features. This aggressive feature selection further allows for reduced bandwidth when streaming sensory data and reduced computational costs of the predictions. We also demonstrate the ability to make online predictions based on the sensory information received to date. We compare PLS to other regression methods, such as principal components regression. Our methods are tested on a WAM manipulator equipped with either a spherical probe or a BarrettHand with arrays of tactile sensors.

Text

2014-10-28

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/50910

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/