CFP: RA-L Special Issue on Learning for Safe and Robust Control
Safety-critical tasks are very common in real-life robotic applications. Tasks are considered safety-critical when, for example, robots act in close proximity to humans, especially when they have limited knowledge about the environment.
The proposed SI hinges on contributions that advance the field of learning for robust and safe control. Our interest encompasses both theoretical and empirical results which aim to show that combining machine learning strategies with model-based controllers is the key to developing stable and robust autonomous systems that are capable of adapting to dynamic environmental changes, are resilient to external disturbances, and unmodelled dynamic effects, and that behave in accordance to the theoretical guarantees of controllers.
See our website for more information.
Key Dates and Deadlines
- Submission deadline: March 31, 2023
- Final Publication: September 27, 2023
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