# Feasibility-aware Learning of Robust Temporal Logic Controllers using BarrierNet

arXiv: https://arxiv.org/abs/2512.06973 · HTML: https://mlsanity.com/paper/2512.06973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Q-Learning for Robust Satisfaction of Signal Temporal Logic Specifications](https://mlsanity.com/paper/1609.07409/) (2016)
- [Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods](https://mlsanity.com/paper/2008.00097/) (2020)
- [On Robustness Metrics for Learning STL Tasks](https://mlsanity.com/paper/2003.06041/) (2020)
- [Tractable Reinforcement Learning of Signal Temporal Logic Objectives](https://mlsanity.com/paper/2001.09467/) (2020)
- [Robust Temporal Logic Model Predictive Control](https://mlsanity.com/paper/1511.00347/) (2015)
- [A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Control](https://mlsanity.com/paper/2006.05239/) (2020)
- [Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specifications](https://mlsanity.com/paper/1903.05186/) (2019)
- [Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints](https://mlsanity.com/paper/2009.11468/) (2020)
- [Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications](https://mlsanity.com/paper/2202.01997/) (2022)
- [Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications](https://mlsanity.com/paper/2109.13377/) (2021)
- [Robust Multi-Agent Coordination from CaTL+ Specifications](https://mlsanity.com/paper/2210.01732/) (2022)
- [Learning Robust and Correct Controllers from Signal Temporal Logic Specifications Using BarrierNet](https://mlsanity.com/paper/2304.06160/) (2023)
- [Soft-Minimum and Soft-Maximum Barrier Functions for Safety with Actuation Constraints](https://mlsanity.com/paper/2305.10620/) (2023)
- [TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2510.00225/) (2025)
- [Compositions of Multiple Control Barrier Functions Under Input Constraints](https://mlsanity.com/paper/2210.01354/) (2022)
- [Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications](https://mlsanity.com/paper/1903.06972/) (2019)
- [High Order Control Lyapunov-Barrier Functions for Temporal Logic Specifications](https://mlsanity.com/paper/2102.06787/) (2021)
- [Safe Model-based Control from Signal Temporal Logic Specifications Using Recurrent Neural Networks](https://mlsanity.com/paper/2103.15938/) (2021)
- [Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation](https://mlsanity.com/paper/2303.12966/) (2023)
- [Auxiliary-Variable Adaptive Control Barrier Functions for Safety Critical Systems](https://mlsanity.com/paper/2304.00372/) (2023)
- [Auxiliary-Variable Adaptive Control Lyapunov Barrier Functions for Spatio-Temporally Constrained Safety-Critical Applications](https://mlsanity.com/paper/2404.00881/) (2024)
- [Auxiliary-Variable Adaptive Control Barrier Functions](https://mlsanity.com/paper/2502.15026/) (2025)
- [Sampling-Aware Control Barrier Functions for Safety-Critical and Finite-Time Constrained Control](https://mlsanity.com/paper/2511.11897/) (2025)

## Abstract (arXiv)

Control Barrier Functions (CBFs) have been used to enforce safety and task specifications expressed in Signal Temporal Logic (STL). However, existing CBF-STL approaches typically rely on fixed hyperparameters and per-step optimization, which can lead to overly conservative behavior, infeasibility near tight input limits, and difficulty satisfying long-horizon STL tasks. To address these limitations, we propose a feasibility-aware learning framework that constructs trainable, time-varying High Order Control Barrier Function (HOCBF) constraints and hyperparameters that guarantee satisfaction of a given STL specification. We introduce a unified robustness measure that jointly captures STL satisfaction, constraint feasibility, and control-bound compliance, and propose a neural network architecture to generate control inputs that maximize this robustness. The resulting controller guarantees STL satisfaction with strictly feasible HOCBF constraints and requires no manual tuning. Simulation results demonstrate that the proposed framework maintains high STL robustness under tight input bounds and significantly outperforms fixed-parameter and non-adaptive baselines in complex environments.