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Improving CMA-ES convergence speed, efficiency, and reliability in noisy robot optimization problems.

Russell M Martin1, Steven H Collins2

  • 1Department of Mechanical Engineering, Stanford University, Stanford, 94305, USA rumartin@stanford.edu.

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|January 28, 2026
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Summary
This summary is machine-generated.

Adaptive Sampling CMA-ES (AS-CMA) optimizes robot policies by dynamically allocating evaluation time, improving speed and reducing costs compared to standard methods. This novel approach enhances efficiency in noisy environments with minimal setup complexity.

Keywords:
Covariance matrix adaptation evolution strategy (CMA-ES)efficient optimizationevolution strategiesexoskeleton optimizationnoisy optimization

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Area of Science:

  • Robotics
  • Optimization Algorithms
  • Machine Learning

Background:

  • Robot policy optimization is time-intensive, with evaluation time impacting speed-accuracy trade-offs.
  • Current methods face challenges with noise and efficiency in complex optimization landscapes.

Purpose of the Study:

  • Introduce Adaptive Sampling CMA-ES (AS-CMA), an enhancement to CMA-ES for improved optimization efficiency.
  • Enable consistent precision by assigning sampling time based on predicted sorting difficulty.

Main Methods:

  • Developed AS-CMA, a novel algorithm supplementing CMA-ES with adaptive sampling time allocation.
  • Compared AS-CMA against CMA-ES with static sampling times and Bayesian optimization in simulated cost landscapes.
  • Validated AS-CMA performance in a real-world exoskeleton optimization experiment.

Main Results:

  • AS-CMA achieved convergence in 98% of runs without parameter tuning.
  • AS-CMA demonstrated 24-65% faster convergence and 29-76% lower total cost than optimized CMA-ES.
  • AS-CMA showed superior efficiency and reliability in complex landscapes compared to Bayesian optimization.

Conclusions:

  • AS-CMA enhances optimization efficiency and reliability, particularly in noisy or complex environments.
  • The adaptive sampling strategy offers a practical improvement over static sampling times.
  • AS-CMA minimally increases setup complexity and tuning requirements for optimization tasks.