Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
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Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.
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Domain adaptation could be useful for AIoT applications when deploying models to new scenarios or new working modes of machines [128], [203], [204], [205], [206], e.g., 'synthetic → real', 'daytime → nighttime' or 'clear → rainy'.
“Domain adaptation could be useful for AIoT applications when deploying models to new scenarios or new working modes of machines [128], [203], [204], [205], [206], e.g., 'synthetic → real', 'daytime → nighttime' or 'clear → rainy'.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [204]
Bousmalis et al. propose a deep robotic grasping model named GraspGAN, which bridges the domain gap between synthetic images and real-world ones via pixel-level image translation and a feature-level domain classifier.
“For example, Bousmalis et al. propose a deep robotic grasping model named GraspGAN [204], which bridges the domain gap between synthetic images and real-world ones via the pixel-level image translation and a feature-level domain classifier.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [204]
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“For instance, Generative Adversarial Networks (GANs) [3, 24, 27, 57] are trained to modify images generated in simulations so they resemble the style of real-world images.”
Self-citation
- Authors
- Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., Konolige, K., Levine, S., & Vanhoucke, V.
- Affiliations
- Google (United States) (US)
- Stanford University (US)
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 4243-4250
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2758237641
- Cited by
- 636
- References
- 32
- Citations by year
- 2026: 24 · 2025: 46 · 2024: 47 · 2023: 62 · 2022: 92 · 2021: 93 · 2020: 110 · 2019: 108 · 2018: 46 · 2017: 5 · 2016: 1 · 2012: 1
- FWCI
- 133.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- robotic grasping, domain adaptation, sim-to-real transfer, synthetic data, monocular RGB images, object grasping
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