Robots tackling complex tasks often rely on multiple perception models that balance accuracy and latency. While existing works focus on training such models, they typically target single-step tasks and report mean accuracy, which is insufficient for multi-step control scenarios. The key challenge—known as the model selection problem—is deciding which model to use at each step to optimize both control performance and perception efficiency. We address this by formulating the problem as a multi-objective optimization and provide a provably optimal solution. Using a photo-realistic drone landing task in AirSim, our method reduced control cost by 38.04% and perception time by 79.1% compared to other approaches.
Publisher
Allerton Conference on Communication, Control, and Computing
Series/Report Name or Number
2025 61st Allerton Conference on Communication, Control, and Computing Proceedings
ISSN
2836-4503
Type of Resource
Text
Genre of Resource
Conference Paper/Presentation
Language
eng
Handle URL
https://hdl.handle.net/2142/130330&&
Copyright and License Information
Copyright 2025 is held by Bineet Ghosh and Parasara Sridhar Duggirala.
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