Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

Rika Antonova, Mia Kokic, Johannes A. Stork, Danica Kragic
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:641-650, 2018.

Abstract

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.

Cite this Paper

BibTeX
@InProceedings{pmlr-v87-antonova18a, title = {Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation}, author = {Antonova, Rika and Kokic, Mia and Stork, Johannes A. and Kragic, Danica}, booktitle = {Proceedings of The 2nd Conference on Robot Learning}, pages = {641--650}, year = {2018}, editor = {Billard, Aude and Dragan, Anca and Peters, Jan and Morimoto, Jun}, volume = {87}, series = {Proceedings of Machine Learning Research}, month = {29--31 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v87/antonova18a/antonova18a.pdf}, url = {https://proceedings.mlr.press/v87/antonova18a.html}, abstract = {We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects. } }
Endnote
%0 Conference Paper %T Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation %A Rika Antonova %A Mia Kokic %A Johannes A. Stork %A Danica Kragic %B Proceedings of The 2nd Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2018 %E Aude Billard %E Anca Dragan %E Jan Peters %E Jun Morimoto %F pmlr-v87-antonova18a %I PMLR %P 641--650 %U https://proceedings.mlr.press/v87/antonova18a.html %V 87 %X We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.
APA
Antonova, R., Kokic, M., Stork, J.A. & Kragic, D.. (2018). Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation. Proceedings of The 2nd Conference on Robot Learning, in Proceedings of Machine Learning Research 87:641-650 Available from https://proceedings.mlr.press/v87/antonova18a.html.

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