Prof. Dr. Raul Rojas FU Berlin. Learning and Control for Autonomous Robots

Transcription

1 Prof. Dr. Raul Rojas FU Berlin Learning and Control for Autonomous Robots

2 Embodied Intelligence: A new Paradigm for AI - Intelligence needs a body: mechanics - Computer vision in real time - Artificial Energy management Intelligence is the -of Local the subconscious control - Communication between agents - Coordination and team behavior - Adaptation and learning the art and science

3 Robotic Soccer as AI Benchmark RoboCup started with IJCAI 1997 I - Simulation league II Small size league III- Mid-size league IV- Legged league V Humanoid league

4 Small-Size Size Liga 18 cm in diameter

5 Lisbon 2004

6 Kicking the distance

7 Mid-size league four on four field 12 8 meters

8 Lisbon 2004

9 Pressuring the goalie

10 Our small-size size robots

11 Omnidirectional Design

12 Omnidirectional Control

13 Our mid-size robots Omnidirectional vision - Laptop for control - Firewire video camera

14 CAD Design

15 FUXABOT: The Hexapod

16 I Global vision

17 Global vision computer wireless communication global camera

18 The world is colored Team color ball

19 Projective Transformation

20 Automatic camera calibration

21 Illumination artifacts

22 Adaptive color maps

23 Tracking helps computer vision the position of the ball is predicted variable search frame just a few pixels are read

24 Tracking the robots We need the data of the future Data from the past t vision delay communication delay

25 Predict the robot s movement ositions (x 1,y 1 ) (v x,v y, w) 1 (x 2,y 2 ) (v x,v y, w) 2 predictor (x,y,θ ) (x 3,y 3 ) (v x,v y, w) 3 (x 4,y 4 ) Position and orientation four frames in the future (100 ms) ommands (v x,v y, w) 4

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27 II Local vision

28 Our first omnivision robots

29 Spherical and parabolic transformations

30 The field seen with our mirror

31 Locating the robot Parabolic Mirror distance pixel distance

32 Expectation-Maximization The model attracts the cloud of points

33 Forces on real data

34 Precomputed resultant forces for each coordinate

35 Obstacle Detection

36 Obstacle Modelling

37 Obstacle Fusion

38 Obstacle Identification

39 III Reactive Behavior

40 Reactive Behavior Control slow fast sensors behaviors actuators

41 Lower layer Structure of a layer Higher layer nsors effectors sensors behaviors actors

42 Kicking reflex Kicking reflex activated

43 Screenshot of control software

44 Kicking Path planning objective desired position position approach Path planning stop execute

45 Kicking aproach objective position Ziel approach Path planning stop execute

46 Kicking objective aproach position Ziel approach Path planning stop execute

47 Kicking objective execute position approach Path planning stop execute

48 Kicking objective execute Halten approach Path planning stop execute

49 Kicking objective execute stop approach Path planning stop execute

50 Kicking stop approach Path planning stop execute

51 Pass Pass approach goal goal position approach position

52 Pass Pass goal position goal aproach position pass

53 Pass pass goal kick-it goal approach position pass kick-it

54 Pass kick-it approach position pass kick-it

55 Pass kick-it approach position pass kick-it

56 Pass kick-it approach position pass kick-it

57 IV Learning and Coaching the robots

58 Anpassbarkeit

59 Raumfreiheit

60 Beispiel-Eingabe

61 Learning to pass

62 Passing game

63 Team Play

64 The Goalie

65 Goalie again

66 Learning: : robot heal yourself

67 Learn what the robot does positions (x 1,y 1 ) (v x,v y, w) 1 (x 2,y 2 ) (v x,v y, w) 2 (x 3,y 3 ) predictor (x,y,θ ) Measured position (v x,v y, w) 3 (x 4,y 4 ) commands (v x,v y, w) 4

68 Invert the prediction (x 1,y 1 ) (v x,v y, w) 1 (x 2,y 2 ) (v x,v y, w) 2 (x 3,y 3 ) predictor (x,y,θ ) desired position (v x,v y, w) 3 (x 4,y 4 ) (v x,v y, w) 4

69 One burnt motor

70 V Summary and Outlook

71 FU Fighters 1999 Vizeweltmeister 2000 Europa- und Vizeweltmeister 2001 Vierter Platz 2002 Europa- und Vizeweltmeister 2003 Europameister Dritter Platz (small( small-size) Halbfinalist (mid( mid-size) 2004 Weltmeister (small( small-size)

72 Gene s Law Embedded Intelligence

73 2005: Intelligent Vehicles

74 Intelligent Chalkboard

75 Mathematical Handwriting Recognition

76 Intelligent Glasses Camera captures images Software stitches images Speech recognition Speechsynthesis

77 Small-Size Team Anna Egorova, Alexander Gloye, Mark Simon, Cüneyt Göktekin, Bastian Hecht, Achim Liers, Oliver Tenchio, Fabian Wiesel, Lina Ourima, Maria Jütte, Thomas Sunderman Susanne Schöttker-Söhl

78 Mid-size Team Holger Freyther, Ketill Gunnarsson, Henning Heinold, Felix von Hundelshausen, Wolf Lindstrot, Marian Luft, Slav Petrov, Michael Schreiber, Frederik Zilly, Fabian Ruff, David Schneider, Markus Kettern

79 Detlef MüllerM und Feinwerktechnik

80 Fritz-Haber Haber-Institut Georg Heyne Peter Zilske Torsten Vetter Ronald Nehring

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82 History of Computing