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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Weighted Maximum Likelihood for Controller Tuning |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 979-8-3503-2365-8 |
| ISSN | 1050-4729 |
| Page Range | 1334 - 1341 |
| Event Title | 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 |
| Event Type | conference |
| Event Location | London, United Kingdom of Great Britain and Northern Ireland |
| Event Start Date | May 29 - 2023 |
| Event End Date | June 2 - 2023 |
| Series Name | IEEE International Conference on Robotics and Automation. Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers |
| Abstract Text | Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off between progress maximization and path following at runtime without relying on globally optimized trajectories. However, finding the optimal set of tuning parameters for MPCC is challenging because (i) the full quadrotor dynamics are non-linear, (ii) the cost function is highly non-convex, and (iii) of the high dimensionality of the hyperparameter space. This paper leverages a probabilistic Policy Search method—Weighted Maximum Likelihood (WML)—to automatically learn the optimal objective for MPCC. WML is sample-efficient due to its closed-form solution for updating the learning parameters. Additionally, the data efficiency provided by the use of a model-based approach allows us to directly train in a high-fidelity simulator, which in turn makes our approach able to transfer zero-shot to the real world. We validate our approach in the real world, where we show that our method outperforms both the previous manually tuned controller and the state-of-the-art auto-tuning baseline reaching speeds of 75 km/h. |
| Digital Object Identifier | 10.1109/ICRA48891.2023.10161417 |
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