# Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications

arXiv: https://arxiv.org/abs/2403.16593 · HTML: https://mlsanity.com/paper/2403.16593/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 3 of 3)

- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC](https://mlsanity.com/paper/2109.09910/) (2021)
- [Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving](https://mlsanity.com/paper/2110.04052/) (2021)

## Built on by (most cited first, 1 of 1)

- [Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric](https://mlsanity.com/paper/2607.23532/) (2026)

## 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.