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PFN の深層学習を用いた ロボット研究最前線

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2018年9月13日(木)16:00-16:50@GTC Japan 2018
タイトル:PFN の深層学習を用いた ロボット研究最前線

Published in: Technology
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PFN の深層学習を用いた ロボット研究最前線

  1. 1. (Kuniyuki Takahashi) < > - 2007.4 - 2017.3 (B.S. ) - 2015.2 - 2016.3, 2017.2-2017.3 , Twitter: @Georgekt0927 ↑ 2
  2. 2. Preferred Networks (PFN) • • • • • • Deep learning Industrial IoT Manufacturing Automotive Healthcare 3
  3. 3. GPU MN-1a & MN-1b credits: gpu by Misha Petrishchev from the Noun Project Network Switch by Creaticca Creative Agency from the Noun Project 4
  4. 4. http://www.appraccel.com/blog/2017/1/4/freeing-up-humans-to-do-meaningful-work … http://toyota.jp/mirai/grade/http://toyota.jp/mirai/grade/ 5
  5. 5. http://www.appraccel.com/blog/2017/1/4/freeing-up-humans-to-do-meaningful-work 6
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  16. 16. 100 91,590 77,770 17
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  20. 20. 1x 21
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  23. 23. ! 24
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  28. 28. 29
  29. 29. Tomo et al 2016 30
  30. 30. 31
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  32. 32. & & 33
  33. 33. 20 21 19 34
  34. 34. ? 35
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  37. 37. - - - 38
  38. 38. https://www.irasutoya.com/ 39
  39. 39. • 40
  40. 40. • – • 41
  41. 41. • – • – [Tobin et al. 2017] – [Pinto et al. 2017] 42
  42. 42. l IT&E • IT&E • l IT&E l DDPG 43
  43. 43. Deep Deterministic Policy Gradient • • (actor) (critic) • critic Bellman TD Q • actor critic 44
  44. 44. 45
  45. 45. 46
  46. 46. : 47
  47. 47. • – – • – – – :20cm 48
  48. 48. 9030 policies stored in the 6- dim discrete map 49
  49. 49. 1 70 71 72 73 74 75 701 702 703 704 705 712 713 714 715 723 724 725 734 735 745 D1 D3 D5 D7 D9 S1 S3 S5 S7 S9 S11 S13 S15 0 10 20 30 40 1umberof7riDls 1 70 71 72 73 74 75 701 702 703 704 705 712 713 714 715 723 724 725 734 735 745 D1 D3 D5 D7 D9 S1 S3 S5 S7 S9 S11 S13 S15 0 5 10 15 20 25 SerformDnce[m] 51
  50. 50. 52
  51. 51. • • DDPG (T23) (T12) • 53
  52. 52. 1 70 71 72 73 74 75 701 702 703 704 705 712 713 714 715 723 724 725 734 735 745 D1 D3 D5 D7 D9 S1 S3 S5 S7 S9 S11 S13 S15 0 10 20 30 40 1umberof7riDls • • 54
  53. 53. - - IT&E - 55
  54. 54. 56
  55. 55. 57 “ ” https://www.preferred-networks.jp/ja/jobs

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