Learning From Demonstration Atkeson and Schaal

1. Learning From DemonstrationAtkeson and Schaal Dang, RLAB Feb 28th, 2007

2. Goal • Robot Learning from Demonstration • Small number of human demonstrations • Task level learning (learn intent, not just mimicry) • Explore • Parametric vs. nonparametric learning • role of a priori knowledge Dang, RLAB

3. Known Task • Pendulum swing-up task • Like pole balancing, but more complex • Difficult, but easy to evaluate success • Simplified • Restricted to horz. motion • Impt. variables picked out • Pendulum angle • Pendulum angular velocity • Hand location • Hand velocity • Hand acceleration Dang, RLAB

4. Implementation details • SARCOS 7DOF arm • Stereo Vision, colored ball indicators • 0.12s delay overcome with Kalman filter • Idealized pendulum dynamics • Redundant inverse kinematics and real-time inverse dynamics for control Dang, RLAB

5. Learning • Task composed of two subtasks • Believe that subtask learning accelerates new task learning • 1 Pole Swing up • open-loop • 2 Upright Balance • Feedback • Focus here on swing-up • Balancing already learned Dang, RLAB

6. First approach • Directly mimic human hand movement • Fails • Differences in human and robot capabilities • Improper demonstration (not horizontal) • Imprecise mimicry Dang, RLAB

7. Approach the second • Learn reward • Learn a model • Use human demonstration as seed so a planner can find a good policy Dang, RLAB

8. Learn Task Model • Parametric: • learn parameters via linear regression • Nonparametric • Use Locally Weighted Learning • Given desired variable and a set of possibly relevant input variables • Cross validation to tune meta-parameters Dang, RLAB

9. Swing up • Transition to balance occurs at ± 0.5 radians with angular vel. < 3 rad/sec • Reward function set to make robot want to be like demonstrator Dang, RLAB

10. Parametric • Parameters learned from failure data • Trajectory optimized using human trajectory as seed • SUCCESS Dang, RLAB

11. Nonparametric • Slower, but still successful Dang, RLAB

12. Harder Task • Double pump swing up • Approach fails • Believed to be due to improper modeling of the system • Solved by Dang, RLAB

13. Direct task-level learning • Learn a correction term to add to the target angle • Now target ± (0.5+∆)rad • Use binary search • Worked for parametric • Didn’t for nonparametric • Left region of validity of local models • So, tweak velocity all over • Binary search for coefficient Dang, RLAB

14. Results Dang, RLAB

15. Summary of Technique Succeeds for Math Watch demo, mimic hand None Learn model, optimize demo trajectory Parametric, single Tune model, reoptimize Nonparametric, single Binary search for delta Parametric, double Binary search for c Nonparametric, double Dang, RLAB

16. Discussion points • Reward function was given or learned? • Does task-level direct learning make sense? • Only useful in this task / implementation? • I in PID? • Nonparametrics don’t avoid all modeling errors • Poor planner? • Not enough data? • A priori knowledge • human selects inputs, outputs, control system, perception, model selection, reward function, task segmenting, task factors • It Works! Dang, RLAB