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Toward Optimal Configuration Space Sampling

Seminar talk on the paper by Burns and Brock.

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Toward Optimal Configuration Space Sampling

  1. 1. T O C S S B B  O B Hannes Schulz University of Freiburg, ACS Feb 2008
  2. 2. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  3. 3. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  4. 4. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  5. 5. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  6. 6. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  7. 7. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  8. 8. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  9. 9. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  10. 10. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  11. 11. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  12. 12. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C S World Space Configuration Space http://ford.ieor.berkeley.edu/cspace
  13. 13. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R M Config Space w/ Obstacles
  14. 14. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R M Config Space w/ Obstacles and Samples
  15. 15. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R M Config Space Visibility Road Map w/ Obstacles and Samples
  16. 16. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R M Config Space Visibility Road Map w/ Obstacles Planned Path and Samples
  17. 17. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R M Config Space Visibility Road Map w/ Obstacles Planned Path and Samples How to sample quickly in high-dimensional Config Space?
  18. 18. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S G Uniform
  19. 19. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S S G G Uniform Wavefront single query
  20. 20. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S S G G Uniform Wavefront Model- Guided single query multi-query
  21. 21. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S S G G Uniform Wavefront Model- Guided single query multi-query
  22. 22. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S S Entropy-guided, Model-guided, Bridge-Sampling, G G ... Uniform Wavefront Guided single query multi-query
  23. 23. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion S A S S Entropy-guided, Model-guided, Bridge-Sampling, G G ... Uniform Wavefront Guided single query multi-query In this paper: ? “utility-guided” multi-query
  24. 24. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  25. 25. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  26. 26. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion U C S S F A L
  27. 27. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion U C S S F A L Obstacle Sample Free Space Sample
  28. 28. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion U C S S F A L
  29. 29. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion U C S S F A L Function Approximator: Approximate Model of Config Space Use Model to select next free sample Using all known samples aids active learning
  30. 30. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  31. 31. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion D  “U”   S Component 1 Obstacle Component 2 Entropy: Probability that random sample is in visibility region of particular component
  32. 32. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion D  “U”   S Component 1 Obstacle Component 2 Red Sample: Entropy unchanged, Zero information gain
  33. 33. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion D  “U”   S Component 1 Obstacle Component 2 Entropy: Probability that random sample is in visibility region of particular component
  34. 34. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion D  “U”   S Obstacle Just 1 Component left Red Sample: Less Entropy, Large information gain, high Utility
  35. 35. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  36. 36. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion E C U M  C Component 1 Obstacle Component 2 Application Idea 2: Try to increase connectivity
  37. 37. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion E C U M  C Component 1 Obstacle Component 2 Application Idea 2: Try to increase connectivity
  38. 38. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion E C U M  C Component 1 Obstacle Component 2 Center Point
  39. 39. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion E C U M  C Component 1 Obstacle Component 2
  40. 40. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion W  P S  M Component 1 Obstacle Component 2 Application Idea 1: Exploit model of config space
  41. 41. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion W  P S  M Component 1 Obstacle Component 2 Application Idea 1: Exploit model of config space
  42. 42. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion W  P S  M Component 1 Obstacle Component 2
  43. 43. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  44. 44. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R 3 or 4 Joints with 3 DOF Mobile base (2 DOF) each 2 Joints with 1 / 2 DOF each 9 DOF / 12 DOF 4 DOF / 6 DOF Compare only Sampling strategy until path found Difficulty: Analyzing Overhead of Model, Utility Evaluation
  45. 45. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R Runtimes: 4-DOF mobile manipulator
  46. 46. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion R Fraction of Config Space covered: 9-DOF arm
  47. 47. O 1 I: C S  R 2 T M I  U G S Idea 1: Use Config Space Structure Idea 2: Increase Connectivity 3 S A 4 E: C  S S 5 C  C
  48. 48. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start Initially, just two Samples at start and goal, respectively. What happens?
  49. 49. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start Line between two clusters
  50. 50. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start Sample candidates around mitpoint
  51. 51. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start Model does not provide information, choose any.
  52. 52. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start Green: New model. Suppose same nodes chosen again. What happens?
  53. 53. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start New points cluster around previous points
  54. 54. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion A P P  U-G S? Goal Start “Worst case” scenario
  55. 55. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion C Burns & Brock introduced a sampling algorithm for multi-query planning uses active learning maximizes utility outperforms other algorithms not thoroughly evaluated may have strong dependency on parameters/environment
  56. 56. Intro Two Main Ideas Sampling Algorithm Experiments Comments and Conclusion D ?

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