1. Keyframe-based Learning from Demonstration Anthony Dubis “Keyframe-based Learning from Demonstration – Method and Evaluation” – Akgun, Cakmak, Jiang, and Thomaz

2. What We’ll Cover 1.Learning from Demonstration & Its Types 2.The Proposed Framework 3.The Framework Results and Conclusions Bonus: Video on extension

3. LEARNING FROM DEMONSTRATION & ITS TYPES

4. Learning from Demonstration Teach a robot through successful examples Various options – Teleoperation – Motion capture – Kinesthetic manipulation Paper’s focus: Kinesthetic teaching: Having a human teacher physically guide the robot in performing a skill

5. Two Kinesthetic Input Methods for Demonstrations Draw Letters Using a Mouse (2D) Teaching a robot: – Scoop – Pour – Place

6. Learning from Demonstration Introduction (Traditional) Learning from Demonstration (LfD) Continuous trajectory with two endpoints Trajectory Demonstration (TD) Example

7. Learning from Demonstration Advantages Intuitiveness No correspondence problem No extra instrumentation

8. Learning from Demonstration Disadvantages Users lack experience manipulating robots Noisy movements

9. Keyframe-based Learning from Demonstration Keyframe-based Learning from Demonstration (KLfD) Sparse set of consecutive poses, or critical points Provide start, end, and several in-between Keyframe Demonstration (KD) Example

10. Keyframe-based Learning from Demonstration - Advantages Intuitive for the user Pick poses with care

11. Keyframe-based Learning from Demonstration - Disadvantages User lack of experience in manipulating robots Lack of timing information Complex and curvy movements are difficult to express

12. Hybrid Learning from Demonstration Hybrid Learning from Demonstration (HLfD) Let the user choose whatever suits the situation Hybrid Demonstration (HD) - Example

13. Demonstration Types Trajectory, Keyframe, or Hybrid Demonstrations Convert this data into a Sequential Pose Distribution for skill reproduction

14. KLfD – Proposed Framework Traditional LfD techniques are limited Goal: Create one that can take in TD, KD, HD

15. Implementation Overview Can accept and process input from trajectory, keyframe, or hybrid demonstrations.

16. KLfD – Framework Implementation Overview Trajectory to Keyframe Conversion Temporal Alignment and Clustering – Provides Sequential Pose Distribution (SPD) Skill Reproduction – determine parameters

17. Validity Requirements – Handle trajectory input as well as conventional methods – “Lost” data Compare trajectory demos to baseline: – Gaussian Mixture Model (GMM) to fit the data – Gaussian Mixture Regression (GMR) to reproduce the skill – GMM + GMR

18. Drawing Letters 2D mouse gestures Allows TD, KD, and HD Skills: B, D, G, M, O, P Measurement: alignment cost between generated and goal trajectories KFD GMM

19. Validity - 2D Letters

20. Robot Skills Simon Robot 7 DOF arms 2 DoF torso 13 DoF head Scooping using TD Pouring using TD Placement using KD

22. Validity - Robot Skills

23. Similar scooping and pouring weights KLfD framework is on par with GMM+GMR

24. FRAMEWORK RESULTS & CONCLUSIONS

25. Results – Letter Drawing Comparing Input Types Letter O is all curved, trajectory is best. Letter M is straight, KFD is best

26. Results – Robot Placement

27. Conclusions - Advantages Framework seems to do its job Stacks up against conventional models Accept any of the three inputs to create Sequential Pose Distributions (SPD)

28. Conclusions - Disadvantages Keyframe inputs -> missing velocity parameters Zero velocity and acceleration assumption

29. Extensions Adding Queries by robot PR2