2. Sebastian Thrun Carnegie Mellon University Statistical Learning in Robotics State-of-the-Art, Challenges and Opportunities

3. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics Machine Learning Robotics Machine Learning Robotics Machine Learning

4. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 7 Open Problems Estimation and Learning In Robotics Research Today Robotics Research Today

5. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics Yesterday

6. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics Today

7. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics Tomorrow? Thanks to T. Dietterich

8. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics @ CMU, 1992

9. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics @ CMU, 1994

10. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics @ CMU 1996 With: RWI / iRobot, Hans Nopper

11. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics @ CMU/UBonn, 1997 with W. Burgard, A.B. Cremers, D. Fox, D. Hähnel, G. Lakemeyer, D. Schulz, W. Steiner

12. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics @ CMU, 1998 with M. Beetz, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Hähnel, C. Rosenberg, N. Roy, J. Schulte, D. Schulz

13. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 7 Open Problems Robotics Research Today Estimation and Learning In Robotics

14. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 The Robot Localization Problem Position tracking (error bounded) Global localization (unbounded error) Kidnapping (recovery from failure) ?

15. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Probabilistic Localization p(z 0 | x, m) p(x 0 | z 0, m) p(x 1 |u 1,z 0,m) [Simmons/Koenig 95] [Kaelbling et al 96] [Burgard et al 96] [Thrun et al 96] p(z 1 | x, m) p(x 1 |,z 1,u 1,z 0,m) p(x 0 | m)

16. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Probabilistic Localization Bayes [Kalman 60, Rabiner 85] x = state t = time m = map z = measurement u = control Markov laser datap(z|x,m) map m x t-1 utut p(x t |x t-1,u t )

17. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 What is the Right Representation? Multi-hypothesis [Weckesser et al. 98], [Jensfelt et al. 99] Particles [Kanazawa et al 95] [de Freitas 98] [Isard/Blake 98] [Doucet 98] Kalman filter [Schiele et al. 94], [Weiß et al. 94], [Borenstein 96], [Gutmann et al. 96, 98], [Arras 98] [Nourbakhsh et al. 95], [Simmons et al. 95], [Kaelbling et al. 96], [Burgard et al. 96], [Konolige et al. 99] Histograms (metric, topological)

18. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Monte Carlo Localization (MCL) p(z 0 | x, m) p(x 0 | z 0, m) p(x 1 |u 1,z 0,m) p(z 1 | x, m) p(x 1 |,z 1,u 1,z 0,m) p(x 0 | m)

19. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Monte Carlo Localization (MCL) With: Wolfram Burgard, Dieter Fox, Frank Dellaert

20. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Implications for Planning & Control MDP PlannerPOMDP Planner With N. Roy

21. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Monte Carlo Localization With: Frank Dellaert

22. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002

24. Learning Maps aka Simultaneous Localization and Mapping (SLAM) 70 m

25. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Learning Maps 10 6 dimensions3 dimensions Localization:

26. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Learning Maps with Extended Kalman Filters [Smith, Self, Cheeseman, 1990]

27. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Kalman Filter Mapping: O(N 2 )

28. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Can We Do the Same With Particle Filters? robot poses and maps sample map + pose 

29. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Mapping: Structured Generative Model s1s1 s2s2 stst u2u2 utut m2m2 m1m1 z1z1 z2z2 s3s3 u3u3 z3z3 ztzt... Landmark robot pose control measurement With K. Murphy, B. Wegbreit and D. Koller

30. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Rao-Blackwellized Particle Filters landmark n=2 … landmark n=N landmark n=1 landmark n=2 … landmark n=N landmark n=1 robot poses [Murphy 99, Montemerlo 02]

31. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Ben Wegbreit’s Log-Trick 3,33,3 n  3 ? FT n  2 ? F T n  4 ? F T [i] new particle old particle Michael Montemerlo, Ben Wegbreit, Daphne Koller & Sebastian Thrun

32. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Advantage of Structured PF Solution Kalman: O(N 2 ) 500 features 1,000,000 features Moore’s Theorem: logN  30 Experimental: M=250 Rao-B’ PFs: O(MlogN) + global uncertainty, multimodal + non-linear systems + sampling over data associations

33. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 3 Examples Particles + Kalman filters Particles + Particles Particles + Point Estimators

34. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Outdoor Mapping (no GPS) With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney

35. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney

36. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Tracking Moving Features With: Michael Montemerlo

37. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Tracking Moving Entities Through Map Differencing

38. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Map-Based People Tracking With: Michael Montemerlo

39. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Autonomous People Following With: Michael Montemerlo

40. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Indoor Mapping n Map: point estimators (no uncertainty) n Lazy

41. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Importance of Probabilistic Component Non-probabilisticProbabilistic, with samples

42. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Multi-Robot Exploration DARPA TMR MarylandDARPA TMR Texas With: Reid Simmons and Dieter Fox

43. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Learning Object Models

44. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Nearly Planar Maps Idea: Exploit fact that buildings posses many planar surfaces n Compacter models n Higher Accuracy n Good for capturing environmental change

45. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Online EM and Model Selection mostly planar mapraw data

46. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Online EM and Model Selection CMU Wean HallStanford Gates Hall

47. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 3D Mapping Result With: Christian Martin

48. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Combining Tracking and Mapping With Dirk Hähnel, Dirk Schulz and Wolfram Burgard

49. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Combining Tracking and Mapping With Dirk Hähnel, Dirk Schulz and Wolfram Burgard

50. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Underwater Mapping (with University of Sydney) With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding

51. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Robotics Research Today Estimation and Learning In Robotics 7 Open Problems

52. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Can We Learn Better Maps? n Stationary objects and moving objects, people n Motion characteristics, relational knowledge n Less structured environments (jungle, underwater) n In real-time

53. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Can We Learn Control? n Not an MDP n Not discrete or low-dimensional n Not knowledge-free

54. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 How Can We Learn in Context? Goal of robotics is not … n mapping n classification n clustering n density estimation n reward prediction n … But simply: Doing the right thing.

55. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 How can we exploit Domain Knowledge in Learning? Test: Is hypothesis consistent with n laws of geometry? n laws of physics? n conventional wisdom? n … Domain knowledge is your friend! n ILP? n “Lifelong” learning?

56. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Can we Integrating Learning and Programming? LearningProgramming prob x = {{10, 0.2}, {11, 0.8}}; prob y = {{20, 0.5}, {21, 0.5}}; prob z = x + y; prob f = neuroNet(y); with Frank Pfenning, CMU

57. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 What Can We Learn From Biology? Courtesy of Bill Skaggs, University of Pittsburgh

58. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 University of Pittsburgh School of Nursing Prof. Jackie Dunbar-Jacob Prof. Sandy Engberg Prof. Margo Holm Prof. Deb Lewis Prof. Judy Matthews Prof. Barbara Spier School of Medicine Prof. Neil Resnick Prof. Joan Rogers Intelligent Systems Prof. Don Chiarulli University of Pittsburgh Computer Science Prof. Martha Pollack Carnegie Mellon University Computer Science, Robotics Prof. Sebastian Thrun Prof. Geoff Gordon Human Computer Interaction Prof. Sara Kiesler Financial Support National Science Foundation $1.4M ITR Grant $3.2M ITR Grant …And Can We Actually Do Something Useful?

59. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 The Nursebot Project

60. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Haptic Interface (In Development)

61. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Wizard of Oz Studies By Sara Kiesler, Jenn Goetz

62. Sebastian Thrun Carnegie Mellon UniversityICML July 10-12, 2002 Truly Useful….?