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Konstantinos Bousmalis
Researcher at Google
Publications - 40
Citations - 5549
Konstantinos Bousmalis is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 19, co-authored 38 publications receiving 4324 citations. Previous affiliations of Konstantinos Bousmalis include University of Edinburgh & Lafayette College.
Papers
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Proceedings ArticleDOI
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
TL;DR: In this paper, a generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain by learning in an unsupervised manner a transformation in the pixel space from one domain to another.
Proceedings Article
Domain separation networks
TL;DR: The novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.
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Domain Separation Networks
TL;DR: In this article, the authors propose to explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains.
Proceedings ArticleDOI
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis,Alex Irpan,Paul Wohlhart,Yunfei Bai,Matthew Kelcey,Mrinal Kalakrishnan,Laura Downs,Julian Ibarz,Peter Pastor,Kurt Konolige,Sergey Levine,Vincent Vanhoucke +11 more
TL;DR: In this paper, the authors 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, and they extensively evaluate their approaches with a total of more than 25,000 physical test grasps, including a novel extension of pixel-level domain adaptation that they termed the GraspGAN.
Proceedings ArticleDOI
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James,Paul Wohlhart,Mrinal Kalakrishnan,Dmitry Kalashnikov,Alex Irpan,Julian Ibarz,Sergey Levine,Raia Hadsell,Konstantinos Bousmalis +8 more
TL;DR: This paper presents Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data and learns to translate randomized rendered images into their equivalent non-randomized, canonical versions.