Learning Interactively to Resolve Ambiguity in Reference Frame Selection

Learning Interactively to Resolve Ambiguity in Reference Frame Selection

Franzese, G. (TU Delft Learning & Autonomous Control)
Celemin, Carlos (TU Delft Learning & Autonomous Control)
Kober, J. (TU Delft Learning & Autonomous Control)

2020

In Learning from Demonstrations, ambiguities can lead to bad generalization of the learned policy. This paper proposes a framework called Learning Interactively to Resolve Ambiguity (LIRA), that recognizes ambiguous situations, in which more than one action have similar probabilities, avoids a random action selection, and uses the human feedback for solving them. The aim is to improve the user experience, the learning performance and safety. LIRA is tested in the selection of the right goal of Movement Primitives (MP) out of a candidate list if multiple contradictory generalizations of the demonstration(s) are possible. The framework is validated on different pick and place operations on a Emika-Franka Robot. A user study showed a significant reduction on the task load of the user, compared to a system that does not allow interactive resolution of ambiguities.

Active Learning
Human Robot Interaction
Learning from Demonstrations
User-friendly Robot Learning

http://resolver.tudelft.nl/uuid:b26e571f-8018-4170-a524-215116cd01eb

2640-3498

Proceedings of Machine Learning Research, 155, 1298-1311

4th Conference on Robot Learning, CoRL 2020, 2020-11-16 → 2020-11-18, Virtual, Online, United States

Institutional Repository

journal article

© 2020 G. Franzese, Carlos Celemin, J. Kober