Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications
On the map: Offline RL and imitation learning from data → Robot learning from demonstration
Abstract (arXiv)
We present a novel method for imitation learning for control requirements expressed using Signal Temporal Logic (STL). More concretely we focus on the problem of training a neural network to imitate a complex controller. The learning process is guided by efficient data aggregation based on counter-examples and a coverage measure. Moreover, we introduce a method to evaluate the performance of the learned controller via parameterization and parameter estimation of the STL requirements. We demonstrate our approach with a flying robot case study.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 3 of its 23 references that are in the map. First posted on arXiv 25 Mar 2024.
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Citing papers per year: 2026: 1.
1 paper.
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