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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | User-Conditioned Neural Control Policies for Mobile Robotics |
| 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 | 1342 - 1348 |
| 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, learning-based controllers have been shown to push mobile robotic systems to their limits and provide the robustness needed for many real-world applications. However, only classical optimization-based control frameworks offer the inherent flexibility to be dynamically adjusted during execution by, for example, setting target speeds or actuator limits. We present a framework to overcome this shortcoming of neural controllers by conditioning them on an auxiliary input. This advance is enabled by including a feature-wise linear modulation layer (FiLM). We use model-free reinforcement-learning to train quadrotor control policies for the task of navigating through a sequence of waypoints in minimum time. By conditioning the policy on the maximum available thrust or the viewing direction relative to the next waypoint, a user can regulate the aggressiveness of the quadrotor's flight during deployment. We demonstrate in simulation and in real-world experiments that a single control policy can achieve close to time-optimal flight performance across the entire performance envelope of the robot, reaching up to 60 km/h and 4.5 g in acceleration. The ability to guide a learned controller during task execution has implications beyond agile quadrotor flight, as conditioning the control policy on human intent helps safely bringing learning based systems out of the well-defined laboratory environment into the wild. |
| Digital Object Identifier | 10.1109/ICRA48891.2023.10160851 |
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