Commentary

Pomerleau, Andrée

doi:10.1159/000276464

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…We describe the proposed method for learning depth image augmentations in Sections III-B and III-C. Our method builds on Behaviour Cloning (BC) policy learning [23], [25] which we overview in Section III-A.…”

Section: Approachmentioning

confidence: 99%

Learning to Augment Synthetic Images for Sim2Real Policy Transfer

Pashevich

Strudel

Kalevatykh

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain-specific visual data. While collecting such data from real robots is possible, such an approach limits the scalability as learning policies typically requires thousands of trials.In this work we attempt to learn manipulation policies in simulated environments. Simulators enable scalability and provide access to the underlying world state during training. Policies learned in simulators, however, do not transfer well to real scenes given the domain gap between real and synthetic data. We follow recent work on domain randomization and augment synthetic images with sequences of random transformations. Our main contribution is to optimize the augmentation strategy for sim2real transfer and to enable domain-independent policy learning. We design an efficient search for depth image augmentations using object localization as a proxy task. Given the resulting sequence of random transformations, we use it to augment synthetic depth images during policy learning. Our augmentation strategy is policy-independent and enables policy learning with no real images. We demonstrate our approach to significantly improve accuracy on three manipulation tasks evaluated on a real robot.

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Section: Approachmentioning

confidence: 99%