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Optimization of a Dynamic Vaulting Behavior for LittleDog

This study focuses on optimizing a dynamic behavior for the LittleDog robot to traverse a large barrier at maximum speed. The parameterized behavior involves independent trajectory planning for front and back leg pairs using fixed positions and time scales perturbed by gaussians. Optimization is done through genetic algorithms and fitness function based on distance traveled. The research also explores robustness issues between simulator and real-world performance by optimizing over different simulator properties.

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Optimization of a Dynamic Vaulting Behavior for LittleDog

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  1. Alex Grubb and Nathan Ratliff ACRL 04/28/09 Optimization of a Dynamic Vaulting Behavior for LittleDog

  2. Task + Approach • Traverse a large barrier using the LittleDog robot with maximum speed. • We seek to optimize a parameterized dynamic behavior to vault over the barrier.

  3. Parameterization • Independent trajectory for front and back leg pairs. • Each joint trajectory given fixed start and end positions and fixed time scale. • Trajectory is perturbed by three gaussians with parameterized time position and magnitude.

  4. Optimization • Previous trajectory parameterization optimized using genetic algorithm • Initially optimized gains as well but this led to unreasonably high gains and instability in both the simulator and real robot. • Uses distance traveled as fitness function, given that time scale is fixed.

  5. Preliminary Results

  6. Other Learned Behaviors

  7. Other Learned Behaviors

  8. Robustness • Simulator differs substantially from the real world, leading to substantially different real world performance. • Currently focusing on optimizing over a set of sampled simulators with slightly different frictional properties, etc. to maximize robustness of learned behavior.

  9. Questions?

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