ECAI 2004 Conference Paper

[full paper]

An active learning approach for assessing robot grasp reliability

Antonio Morales, Eris Chinellato, Andrew H. Fagg, Angel P. del Pobil

This paper describes a novel application of active learning techniques in the field of robotic grasping. A vision-based grasping system has been implemented on a humanoid robot. It is able to compute a set of feasible grasps and to execute and measure the actual reliability of any of them. An algorithm aimed to predict the performance of an untested grasp using the results observed on previous similar attempts is presented. The previous experience is stored using a set of vision-based grasp descriptors. In addition to this, an algorithm that actively selects the next grasp to be executed in order to improve the predictive quality of the accumulated experience is introduced as well. An exhaustive database of experimental data is collected and used to test and validate both algorithms.

Keywords: robotics, active learning, Machine Learning

Citation: Antonio Morales, Eris Chinellato, Andrew H. Fagg, Angel P. del Pobil: An active learning approach for assessing robot grasp reliability. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.905-909.