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Optimization of a Dynamic Vaulting Behavior for LittleDog

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

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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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