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Statistical Learning in Robotics State-of-the-Art, Challenges and Opportunities

Statistical Learning in Robotics State-of-the-Art, Challenges and Opportunities. Sebastian Thrun Carnegie Mellon University. This Talk. Robotics Research Today. Robotics Research Today. Estimation and Learning In Robotics. 7 Open Problems. Robotics Yesterday. Robotics Today.

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Statistical Learning in Robotics State-of-the-Art, Challenges and Opportunities

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  1. Statistical Learning in RoboticsState-of-the-Art, Challenges and Opportunities Sebastian Thrun Carnegie Mellon University

  2. This Talk Robotics Research Today Robotics Research Today Estimation and Learning In Robotics 7 Open Problems

  3. Robotics Yesterday

  4. Robotics Today

  5. Robotics Tomorrow? Thanks to T. Dietterich

  6. Robotics @ CMU, 1992

  7. Robotics @ CMU, 1994

  8. Robotics @ CMU 1996 With: RWI / iRobot, Hans Nopper

  9. Robotics @ CMU/UBonn, 1997 with W. Burgard, A.B. Cremers, D. Fox, D. Hähnel, G. Lakemeyer, D. Schulz, W. Steiner

  10. 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

  11. This Talk Robotics Research Today Estimation and Learning In Robotics 7 Open Problems

  12. The Robot Localization Problem ? • Position tracking (error bounded) • Global localization (unbounded error) • Kidnapping (recovery from failure)

  13. Probabilistic Localization p(x0 | m) p(z0 | x, m) p(x0 | z0, m) p(x1|u1,z0,m) p(z1 | x, m) p(x1| ,z1 ,u1,z0,m) [Simmons/Koenig 95] [Kaelbling et al 96] [Burgard et al 96] [Thrun et al 96]

  14. Probabilistic Localization x = state t = time m = map z = measurement u = control p(xt|xt-1,ut) xt-1 ut Bayes Markov map m laser data p(z|x,m) Markov [Kalman 60, Rabiner 85]

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

  16. Monte Carlo Localization (MCL) p(x0 | m) p(z0 | x, m) p(x0 | z0, m) p(x1|u1,z0,m) p(z1 | x, m) p(x1| ,z1 ,u1,z0,m)

  17. Monte Carlo Localization (MCL) With: Wolfram Burgard, Dieter Fox, Frank Dellaert

  18. Implications for Planning & Control MDP Planner POMDP Planner With N. Roy

  19. Monte Carlo Localization With: Frank Dellaert

  20. Learning Mapsaka Simultaneous Localization and Mapping (SLAM) 70 m

  21. Learning Maps 3 dimensions 106 dimensions Localization:

  22. Learning Maps with Extended Kalman Filters [Smith, Self, Cheeseman, 1990]

  23. Kalman Filter Mapping: O(N2)

  24. Can We Do the Same WithParticle Filters?  sample map + pose robot poses and maps

  25. Mapping: Structured Generative Model Landmark m1 z1 z3 measurement . . . s1 s2 s3 st robot pose u3 ut u2 control z2 zt m2 With K. Murphy, B. Wegbreit and D. Koller

  26. Rao-Blackwellized Particle Filters  … robot poses landmark n=1 landmark n=N landmark n=2 … landmark n=1 landmark n=N landmark n=2 [Murphy 99, Montemerlo 02]

  27. Ben Wegbreit’s Log-Trick n  4 ? T F new particle n  2 ? F T n  3 ? T F [i] [i] m3,S3 n  4 ? k  4 ? T T F F old particle n  2 ? k  2 ? n  6 ? k  6 ? T T F F T T F F k  1 ? n  1 ? n  3 ? k  3 ? n  5 ? k  1 ? k  3 ? n  7 ? T T F F T T F F T T F F T T F F [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] [i] m1,S1 m1,S1 m2,S2 m2,S2 m3,S3 m3,S3 m4,S4 m4,S4 m5,S5 m5,S5 m6,S6 m6,S6 m7,S7 m7,S7 m8,S8 m8,S8 Michael Montemerlo, Ben Wegbreit, Daphne Koller & Sebastian Thrun

  28. Advantage of Structured PF Solution Rao-B’ PFs: O(MlogN) Moore’s Theorem: logN 30 Experimental: M=250 1,000,000 features + global uncertainty, multimodal + non-linear systems + sampling over data associations Kalman: O(N2) 500 features

  29. 3 Examples Particles + Kalman filters Particles + Particles Particles + Point Estimators

  30. Outdoor Mapping (no GPS) With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney

  31. With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney

  32. Tracking Moving Features With: Michael Montemerlo

  33. Tracking Moving Entities Through Map Differencing

  34. Map-Based People Tracking With: Michael Montemerlo

  35. Autonomous People Following With: Michael Montemerlo

  36. Indoor Mapping • Map: point estimators (no uncertainty) • Lazy

  37. Importance of Probabilistic Component Non-probabilistic Probabilistic, with samples

  38. Multi-Robot Exploration DARPA TMR Texas DARPA TMR Maryland With: Reid Simmons and Dieter Fox

  39. Learning Object Models

  40. Nearly Planar Maps Idea: Exploit fact that buildings posses many planar surfaces • Compacter models • Higher Accuracy • Good for capturing environmental change

  41. Online EM and Model Selection raw data mostly planar map

  42. Online EM and Model Selection CMU Wean Hall Stanford Gates Hall

  43. 3D Mapping Result With: Christian Martin

  44. Combining Tracking and Mapping With Dirk Hähnel, Dirk Schulz and Wolfram Burgard

  45. Combining Tracking and Mapping With Dirk Hähnel, Dirk Schulz and Wolfram Burgard

  46. Underwater Mapping (with University of Sydney) With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding

  47. This Talk Robotics Research Today Estimation and Learning In Robotics 7 Open Problems

  48. Can We Learn Better Maps? • Stationary objects and moving objects, people • Motion characteristics, relational knowledge • Less structured environments (jungle, underwater) • In real-time #1

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