A talk from the Develop Track at AWE EU 2018 - the World's #1 XR Conference & Expo in Munich, Germany 18 -19, October, 2018. Chris Wróbel (vRational): Using VR and Imitation Machine Learning Algorithms for Training Robots Robotics and VR have a huge potential to be used hand-in-hand in example for improving industrial processes with teleoperation or human-curated robot trainings. In this talk, I will explain how to incorporate Machine Learning training into Unity projects and additionally use VR control for imitation learning algorithms accessible in ML Agents library. Besides, I will share the story of improving VR game experience with the UR3 industrial robot „Marvin”. http://AugmentedWorldExpo.com

1. U S I N G V R A N D I M I TAT I O N
L E A R N I N G F O R T R A I N I N G R O B O T S
C H R I S W R O B E L , V R AT I O N A L

2. Consultants and Experts
for VR, AR, ML and Robotics

3. T H E B E G I N N I N G
E N H A N C I N G V R E X P E R I E N C E
W I T H A P H Y S I C A L R O B O T
“ M A R V I N ”

6. I N S P I R AT I O N - M C E V R

7. P R O G R A M M I N G R O B O T S
I S E A S Y A N D C H E A P F O R
R E P E ATA B L E TA S K S …

8. S O U R C E : H T T P S : / / W W W. U N I V E R S A L - R O B O T S . C O M / P R O D U C T S / U R 5 - R O B O T /

9. B U T D I F F I C U LT A N D
E X P E N S I V E F O R R A P I D LY
C H A N G I N G A C T I O N S …

10. S O U R C E : H T T P S : / / W W W. Y O U T U B E . C O M / WAT C H ? V = F R J 3 4 O 4 H N 4 I

13. M A C H I N E L E A R N I N G I N
S I M U L AT E D
E N V I R O N M E N T S

14. M L - A G E N T S T O O L K I T
( B E TA )

15. R E I N F O R C E M E N T L E A R N I N G C Y C L E
E N V I R O N M E N T A G E N T
State & Reward
Action
State
Transition

17. C H A L L E N G E # 1 :
T E A C H I N G A R O B O T T O WA L K

18. R E WA R D
K E E P H E A D A S H I G H A S
P O S S I B L E A N D M O V E
F O R WA R D

20. C O D E

21. A F T E R 5 H O U R S …

23. C H A L L E N G E # 2 :
T E A C H I N G A C O L L A B O R AT I V E
R O B O T A R M T O B A L A N C E A B A L L

24. R E WA R D
C AT C H A B A L L O N A
P L AT E A N D K E E P I T A S
L O N G A S P O S S I B L E

26. F I N A L R E S U LT ( > 2 0 M I N )

29. T H E T R U T H
I T W O R K E D W E L L O N LY I F
A B A L L WA S D R O P P E D
F R O M T H E S A M E S P O T

30. N E W I D E A !
C O M B I N G I N G A G E N T S

32. C H A L L E N G E S
• Programming Skills required
• GLOBAL VS RELATIVE
• NORMALIZATIONS
• ROTATIONS - EULER vs QUATERTNIONS
• Long Learning Cycle
• >20 hours of coding and debugging

33. I S T H E R E A B E T T E R
WAY ?

34. U S I N G V R F O R
I M I TAT I O N L E A R N I N G

35. M I M I C K I N G T H E
B E H AV I O U R O F T H E
H U M A N M O V E M E N T S

36. C H A L L E N G E # 2 - R E V I S E D
T E A C H I N G A C O L L A B O R AT I V E
R O B O T T O B A L A N C E A B A L L

37. U S I N G V R ( O R A R ) T O O P E R AT E A
R O B O T

38. 2 M I N T R A I N I N G C Y C L E

40. C H A L L E N G E # 3
T E A C H I N G A C O L L A B O R AT I V E
R O B O T T O F L I P A PA N C A K E

43. A D VA N TA G E S
• Programming Skills not required
• Shorter Learning Cycle

44. W H AT ’ S N E X T ?

45. U S I N G M O T I O N
C A P T U R E F O R
T R A I N I N G R O B O T S

47. S I G G R A P H 2 0 1 8
D E E P M I M I C
S C I E N T I S T S F R O M U N I V E R S I T I E S O F
C A L I F O R N I A A N D B R I T I S H C O L U M B I A

51. F I N A L R E M A R K S

52. Decision
Makers
Robotics
Machine
Learning
IoT AR/VR

53. S O O N … T R A I N I N G
R O B O T S W I L L B E
P O S S I B L E W I T H A
P L U G & P L AY S O L U T I O N S

54. I N T H E F U T U R E
C O L L A B O R AT I V E R O B O T S
W I L L B E A S P O P U L A R A S
R O B O T I C VA C U U M C L E A N E R S
T O D AY

55. V R , A R , M L , R O B O T I C S ! ?
L E T ’ S TA L K !
C H R I S @ V R AT I O N A L . C O M

56. B O N U S