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Multi-armed bandit models for 2D grasp planning with uncertainty.

M. Laskey, J. Mahler, Z. McCarthy, F. Pokorny, S. Patil, J. van den Berg, D. Kragic, P. Abbeel, and K. Goldberg. CASE, page 572-579. IEEE, (2015)

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Jeffrey Gaynor

Jeffrey Johnson

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

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Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects.J. Xu, M. Danielczuk, J. Ichnowski, J. Mahler, E. Steinbach, and K. Goldberg. ICRA, page 1546-1552. IEEE, (2020)SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces.M. Laskey, S. Staszak, W. Hsieh, J. Mahler, F. Pokorny, A. Dragan, and K. Goldberg. ICRA, page 462-469. IEEE, (2016)GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming.J. Mahler, S. Patil, B. Kehoe, J. van den Berg, M. Ciocarlie, P. Abbeel, and K. Goldberg. ICRA, page 4919-4926. IEEE, (2015)Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC).E. Solowjow, I. Ugalde, Y. Shahapurkar, J. Aparicio, J. Mahler, V. Satish, K. Goldberg, and H. Claussen. CASE, page 97-103. IEEE, (2020)Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations.M. Laskey, C. Chuck, J. Lee, J. Mahler, S. Krishnan, K. Jamieson, A. Dragan, and K. Goldberg. ICRA, page 358-365. IEEE, (2017)REACH: Reducing False Negatives in Robot Grasp Planning with a Robust Efficient Area Contact Hypothesis Model.M. Danielczuk, J. Xu, J. Mahler, M. Matl, N. Chentanez, and K. Goldberg. ISRR, volume 20 of Springer Proceedings in Advanced Robotics, page 757-772. Springer, (2019)Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot.J. Liang, J. Mahler, M. Laskey, P. Li, and K. Goldberg. CASE, page 1-8. IEEE, (2017)Adversarial Grasp Objects.D. Wang, D. Tseng, P. Li, Y. Jiang, M. Guo, M. Danielczuk, J. Mahler, J. Ichnowski, and K. Goldberg. CASE, page 241-248. IEEE, (2019)Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning.J. Mahler, M. Matl, X. Liu, A. Li, D. Gealy, and K. Goldberg. ICRA, page 1-8. IEEE, (2018)Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics.J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. Ojea, and K. Goldberg. Robotics: Science and Systems, (2017)

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Disambiguation of "Mahler, Jeffrey"

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Multi-armed bandit models for 2D grasp planning with uncertainty.

Please choose a person to relate this publication to

Jeffrey Gaynor

Jeffrey Johnson

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Mahler, Jeffrey"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Multi-armed bandit models for 2D grasp planning with uncertainty.

Please choose a person to relate this publication to

Jeffrey Gaynor

Jeffrey Johnson

Jeffrey Donahue

jeffrey Wimmer

Jeffrey Windisch

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Multi-armed bandit models for 2D grasp planning with uncertainty.