Learning Interactively to Resolve Ambiguity in Reference Frame Selection

Giovanni Franzese, Carlos Celemin, Jens Kober
Proceedings of the 2020 Conference on Robot Learning, PMLR 155:1298-1311, 2021.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v155-franzese21a, title = {Learning Interactively to Resolve Ambiguity in Reference Frame Selection}, author = {Franzese, Giovanni and Celemin, Carlos and Kober, Jens}, booktitle = {Proceedings of the 2020 Conference on Robot Learning}, pages = {1298--1311}, year = {2021}, editor = {Kober, Jens and Ramos, Fabio and Tomlin, Claire}, volume = {155}, series = {Proceedings of Machine Learning Research}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v155/franzese21a/franzese21a.pdf}, url = {https://proceedings.mlr.press/v155/franzese21a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Learning Interactively to Resolve Ambiguity in Reference Frame Selection %A Giovanni Franzese %A Carlos Celemin %A Jens Kober %B Proceedings of the 2020 Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2021 %E Jens Kober %E Fabio Ramos %E Claire Tomlin %F pmlr-v155-franzese21a %I PMLR %P 1298--1311 %U https://proceedings.mlr.press/v155/franzese21a.html %V 155 %X 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.
APA
Franzese, G., Celemin, C. & Kober, J.. (2021). Learning Interactively to Resolve Ambiguity in Reference Frame Selection. Proceedings of the 2020 Conference on Robot Learning, in Proceedings of Machine Learning Research 155:1298-1311 Available from https://proceedings.mlr.press/v155/franzese21a.html.

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