Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Open accessAbstract
Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.
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The main idea of RCAN is to map observations collected on simulated domain as well as those collected on real domain into a common further domain called the canonical domain.
“The main idea is to map the observations collected on the simulated domain as well as those collected on the real domain into a common further domain called the canonical domain .”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [46]
The proposed RCAN approach returns excellent results, however, an effective transfer is not always achieved.
“The proposed approach returns excellent results, however, an effective transfer is not always achieved.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [46]
When the cGAN during training is fed with images sampled only from non-successful trajectories, the final controller results in an unsatisfactory transfer.
“In particular, when the cGAN during training is fed with images sampled only from non-successful trajectories, the nal controller results in an unsatisfactory transfer.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [46]
Section 4.1 (p. 4) says the cGAN’s training images were sampled from QT-Opt trajectories, but does not describe training it only on unsuccessful trajectories. Section 5.2 (p. 7) reports transfer performance for other training approaches, not the claimed unsuccessful-trajectory condition. Searches for failed or unsuccessful trajectory sampling found no result addressing its effect on transfer. (LLM, conf=0.75)
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“…MuJoCo; Fan et al., 2021; Grigsby & Qi, 2020; Hansen & Wang, 2021; Stone et al., 2021; Xing et al., 2021a; Zhang et al., 2018a), and multi-task continuous control benchmarks which could be adapted to ZSG (CausalWorld, RLBench, Meta-world; Ahmed et al., 2020; James et al., 2019a; Yu et al., 2019).”
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Robotic Table Tennis with Model-Free Reinforcement Learning
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Learning to Navigate Sidewalks in Outdoor Environments
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Beyond Top-Grasps Through Scene Completion
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Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard
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Self-citationMarkerless Camera-to-Robot Pose Estimation via Self-Supervised Sim-to-Real Transfer
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Towards Fully-Synthetic Training for Industrial Applications
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Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer
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Positioning of the Robotic Arm Using Different Reinforcement Learning Algorithms
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Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot
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Off-Policy Evaluation via Off-Policy ClassificationPreprint
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Self-citation
- Authors
- James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., & Bousmalis, K.
- Affiliations
- Imperial College London (GB)
- Google (United States) (US)
- Google DeepMind (United Kingdom) (GB)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 12619-12629
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2905464683
- Cited by
- 420
- References
- 76
- Citations by year
- 2026: 25 · 2025: 39 · 2024: 49 · 2023: 62 · 2022: 61 · 2021: 91 · 2020: 66 · 2019: 27
- FWCI
- 23.43
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- sim-to-real transfer, robotic grasping, domain adaptation, domain randomization, reality gap, canonicalization, image-to-image translation, vision-based reinforcement learning, data-efficient training, zero-shot generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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