aiqbench analysis
This paper applies reinforcement learning and Bayesian optimization to automate the design of multirotor drones, focusing specifically on improving performance in agile waypoint navigation tasks. By validating optimized designs in the real world, the study demonstrates effective sim-to-real transferability for task-specific aerial robotics.
Performance-guided Task-specific Optimization for Multirotor Design
arXiv:2510.04724v2 Announce Type: replace Abstract: This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.
Abstract:This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.
Robotics (cs.RO)
arXiv:2510.04724 [cs.RO]
(or arXiv:2510.04724v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.04724 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 198 words
