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Kokic, Mia
Publications (2 of 2) Show all publications
Kokic, M., Antonova, R., Stork, J. A. & Kragic, D. (2018). Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation. In: Proceedings of The 2nd Conference on Robot Learning, PMLR 87: . Paper presented at 2nd Conference on Robot Learning, October 29th-31st, 2018, Zürich, Switzerland. (pp. 641-650).
Open this publication in new window or tab >>Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
2018 (English)In: Proceedings of The 2nd Conference on Robot Learning, PMLR 87, 2018, p. 641-650Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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.

National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-248396 (URN)
Conference
2nd Conference on Robot Learning, October 29th-31st, 2018, Zürich, Switzerland.
Note

QC 20190507

Available from: 2019-04-07 Created: 2019-04-07 Last updated: 2019-05-07Bibliographically approved
Antonova, R., Kokic, M., Stork, J. A. & Kragic, D. (2018). Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation. In: : . Paper presented at 2nd Conference on Robot Learning, Zürich, Switzerland, Oct. 29-31 2018.
Open this publication in new window or tab >>Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-249696 (URN)
Conference
2nd Conference on Robot Learning, Zürich, Switzerland, Oct. 29-31 2018
Note

Contribution/Authorship note: Rika Antonova and Mia Kokic contributed equally

QC 20190520

Available from: 2019-04-17 Created: 2019-04-17 Last updated: 2019-05-20Bibliographically approved
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