Runbot

1. Runbot • Team members: • Marie Bro Duun • Georgious Evangelos • Emre Ozbilge • Antonio Gomez Zamorano • Matej Hoffmann • Supervisor: • Tao Geng

2. Goal • Make the Runbot robot learn to adjust step length • Parameters: • Maximum voltage to hip motors • Extreme angle of hip joint • AEP – anterior extreme position • Emergency goal: make the robot walk without a touch sensor

3. Relationship between parameters • Step length = f(max voltage, hip angle) ? • A nontrivial nonlinear relationship • Stability issue

5. Optimization algorithms • 'Heuristic' • Evolutionary algorithms • Simulated annealing • Gradient ascent methods • Methods with memory – e.g. Q-learning

6. Achievements • Real robot: • Going to target step length from some initial conditions • Simulation • Optimization algorithm testbed – Simulink • Several gradient based optimization methods tailored to the problem

7. Algorithm test bed

10. Pseudocode: • Short/Long Term Error Estimation • Relate Delta Constant to Estimated Error • Parameter selection by randomization • Parameter learning for Short/Long Term Gradient Policy Approach

13. Open questions • Fitness landscape • Can gradient be obtained reliably? • Are there too many local minima? • Fitness vs. stability • Other control parameters? • Step length vs. speed

14. Next steps • Obtain a systematic rough picture of the fitness landscape from the real robot to assess feasibility of different optimization methods (e.g. gradient vs. non-gradient, methods with memory...)‏ • Create a similar landscape in testbed and compare algorithms • Run experiments on real robot

15. Thank you for your attention!