Multi-Objective Optimization of Humanoid Robot Hardware and Control for Multiple Tasks via Genetic Algorithms

Abstract

The optimization of hardware and control of humanoid robots for multiple tasks is still an open challenge due to the competing objectives of different behaviors and the complexity of considering control architectures at the design level of a humanoid robot. In this work, we propose a unified multi-objective optimization framework that jointly optimizes both hardware and hierarchical control architectures of a humanoid robot to enhance performance in multiple tasks. Our method employs a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimal robot morphology and control parameters while balancing trade-offs between diverse task requirements. By leveraging genetic algorithms, we enable the integration of discrete search spaces while overcoming the local minima limitations associated with classical nonlinear optimization techniques. Furthermore, the proposed approach directly incorporates the simulation results, ensuring that hardware optimization is performed considering the system dynamics. We validate our approach by optimizing a humanoid robot for two distinct tasks: walking and payload lifting, leveraging MuJoCo to evaluate the task performances. The proposed framework successfully identifies Pareto-optimal tradeoffs, providing a set of design solutions adaptable to different operational requirements.