1. Learning Control for Dynamically Stable Legged Robots
Russ Tedrake
Seung Lab for Theoretical Neurobiology
1/25/02

2. The Problem
To design control algorithms for walking and running robots
Dynamic stability
In particular, M2

3. Why Learning?
It’s a hard problem! (many DOF, under-actuated, highly nonlinear, nonholonomic constraints, ...)
Uncertainty
Delayed reward
Machine learning gives us tools to directly address these issues
Inspired by biology

4. Our Approach Specify desired trajectory or “sketch”
Neural network controller + plant equations form a closed dynamical system
Optimize parameters to minimize the path error between the sketch and the actual trajectory
Combine hill climbing methods with local brute force search

5. Initial Results: Hopping
Sketch
During
learning

6. Initial Results: Hopping (cont.)
Training
Almost
Complete
Training
Converged

7. Initial Results: Stability
Trained for 10 seconds. Simulated for 11.

8. Initial Results: Flip
Sketch
Learned
Trajectory