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Wabersich, Kim Peter (University of Stuttgart), Toussaint, Marc (University of Stuttgart)

Automatic Testing and MiniMax Optimization of System Parameters for Best Worst-Case Performance

Scheduled for presentation during the Regular session "Motion Planning for Manipulators" (ThDT4), Thursday, October 1, 2015, 15:00−15:15, Saal C1+C2

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Performance Evaluation and Benchmarking, Calibration and Identification

Abstract

Robotic systems typically have numerous parameters, e.g. the choice of planning algorithm, real-valued parameters of motion and vision modules, and control parameters. We consider the problem of optimizing these parameters for best worst-case performance over a range of environments. To this end we first propose to evaluate system parameters by adversarially optimizing over environment parameters to find particularly hard environments. This is then nested in a game-theoretic minimax optimization setting, where an outer-loop aims to find best worst-case system parameters. For both optimization levels we use Bayesian global optimization (GP-UCB) which provides the necessary confidence bounds to handle the stochasticity of the performance. We compare our method (Nested Minimax) with an existing relaxation method we adapted to become applicable in our setting. By construction our approach provides more robustness to performance stochasticity. We demonstrate the method for planning algorithm selection on a pick'n'place application and for control parameter optimization on a triple inverted pendulum for robustness to adversarial perturbations.

 

 

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