1. Learning Sequential Composition Plans Using Reduced Dimensionality Examples Nik A. Melchior AAAI 2009 Spring Symposium Agents that Learn from Human Teachers

2. Nik A. Melchior - AAAI 2009 SS 3/25/092 Robot Manipulation

3. Nik A. Melchior - AAAI 2009 SS 3/25/093 Robotic Assembly Tasks

4. Nik A. Melchior - AAAI 2009 SS 3/25/094 Manual Approach Waypoints relative to the objects being assembled Specify position and orientation Define the path taken by the manipulated object

5. Nik A. Melchior - AAAI 2009 SS 3/25/095 Programming by Demonstration Demonstration Natural way for humans to impart information Good for experienced and inexperienced robot operators Allows planning without modeling environment or expensive/difficult sensors

6. Nik A. Melchior - AAAI 2009 SS 3/25/096 Teach-playback Advantages Intuitive Expressive Disadvantages Reproduces jitter and inefficiency Does not permit generalization

7. Nik A. Melchior - AAAI 2009 SS 3/25/097 Overview of Approach Demonstration trajectories provided by human Determination of safe area for interpolation Region-based planning Dimensionality reduction

8. Nik A. Melchior - AAAI 2009 SS 3/25/098 Overview of Approach Demonstration trajectories provided by human Determination of safe area for interpolation Region-based planning Dimensionality reduction

9. Nik A. Melchior - AAAI 2009 SS 3/25/099 Overview of Approach Demonstration trajectories provided by human Determination of safe area for interpolation Region-based planning Dimensionality reduction

10. Nik A. Melchior - AAAI 2009 SS 3/25/0910 Overview of Approach Demonstration trajectories provided by human Determination of safe area for interpolation Region-based planning Dimensionality reduction

11. Nik A. Melchior - AAAI 2009 SS 3/25/0911 2D Demonstration Application

12. Nik A. Melchior - AAAI 2009 SS 3/25/0912 Safety Determination

13. Nik A. Melchior - AAAI 2009 SS 3/25/0913 Region Decomposition Planning frontier advances backwards from the goal Visibility test ensures that a straight-line motion can safely move from any point on the planning frontier to some point on the previous boundary

14. Nik A. Melchior - AAAI 2009 SS 3/25/0914 Region Decomposition

15. Nik A. Melchior - AAAI 2009 SS 3/25/0915 High dimensional planning Trajectories demonstrated on 7-DOF Barrett WAM arm

16. Nik A. Melchior - AAAI 2009 SS 3/25/0916 Dimensionality Reduction Isomap Geodesic (graph-based) distances are used to compute embedding Graph connectivity (neighbors) determined through k-Nearest Neighbor or Ɛ -ball

17. Nik A. Melchior - AAAI 2009 SS 3/25/0917 Dimensionality Reduction Slalom trajectories Isomap embedding ST-Isomap embedding

18. Nik A. Melchior - AAAI 2009 SS 3/25/0918 2D Embedding ST-Isomap reveals structure of the example trajectories Isomap embeddingST-Isomap embedding Demonstration Trajectories

19. Nik A. Melchior - AAAI 2009 SS 3/25/0919 Execution Plan follows the policy of each region Paths planned in lower dimensionality are executed in the original configuration space Lifting uses the Delaunay triangulation to find the points to interpolate

20. Nik A. Melchior - AAAI 2009 SS 3/25/0920 Future Work Improved neighbor relationships More difficult datasets Active learning Determine when and where new examples are required

21. Nik A. Melchior - AAAI 2009 SS 3/25/0921 Thank you Questions?