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20171207 domain-adaptation

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Introduction a paper

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20171207 domain-adaptation

  1. 1. Introduction of a paper by Bousmalis et al. for efficient training of grasping by simulation ROS Japan UG #19 機械学習・AI勉強会 7th December 2017 1
  2. 2. Confidential センスタイムジャパンAbout me • Name: Taku Yoshioka • Interests: Bayesian inference, machine learning, deep learning and robotics • Robot and ROS: 9 months • Affiliation: SenseTime Japan § Computer vision and deep learning § https://www.sensetime.jp § https://blog.sensetime.jp (lunch blog) § Kyoto, Tokyo § We are hiring! 2
  3. 3. Confidential センスタイムジャパンPrevious study 3 • Learn visual servoing policy with a large-scale data obtained with 14 manipulators • Train CNN for evaluation of success probability of grasping (target)
  4. 4. Confidential センスタイムジャパン 4
  5. 5. Confidential センスタイムジャパンIdea of the study • Using simulated environment to learn visual servoing policy for grasping in real environment • Make simulated features close to real ones by domain adaptation (DA) on: § Feature vector in the evaluation network § Visual appearance of the simulated environment 5 Feature vector Visual appearance
  6. 6. Confidential センスタイムジャパン • Fitting distributions based on random samples, without likelihood function (implicit learning) • Adversarial training (e.g., GAN for generative model) 6 Fitting distributions
  7. 7. Confidential センスタイムジャパンDA in feature space • Domain-adversarial neural net (DANN) [3] 7
  8. 8. Confidential センスタイムジャパンDA on visual appearance 8
  9. 9. Confidential センスタイムジャパンDA on visual appearance 9 • GraspGAN: GAN + Label + Semantics (pixel level) GAN Label Semantics
  10. 10. Confidential センスタイムジャパンExperiment 10 • 8M simulated data + 94K〜9.4M real data • Sim-only, Real-only, Sim + Real • Naïve mix, domain-specific batch normalization (DBN) + visual randomization, DBN + DANN, DBN + DANN + visual randomization, GraspGAN
  11. 11. Confidential センスタイムジャパンResults 11 DANN(-R): Feature DA GraspGAN: Feature DA + Visual DA
  12. 12. Confidential センスタイムジャパン 12 • Findings: § Efficient training by simulated data § Improvement of policy by visual appearance DA • Future study § Consider physical discrepancy § Other sensor (e.g., depth) § Further improvement of success rate of the task
  13. 13. Confidential センスタイムジャパンReferences 13 [1] Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., ... & Levine, S. (2017). Using simulation and domain adaptation to improve efficiency of deep robotic grasping. arXiv preprint arXiv:1709.07857. [2] Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2016). Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 0278364917710318. [3] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1-35.

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