# Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives

arXiv: https://arxiv.org/abs/2210.07439 · HTML: https://mlsanity.com/paper/2210.07439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Reinforcement Learning With Temporal Logic Rewards](https://mlsanity.com/paper/1612.03471/) (2016)
- [Q-Learning for Robust Satisfaction of Signal Temporal Logic Specifications](https://mlsanity.com/paper/1609.07409/) (2016)
- [Robustness of Control Barrier Functions for Safety Critical Control](https://mlsanity.com/paper/1612.01554/) (2016)
- [Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints](https://mlsanity.com/paper/2009.11468/) (2020)
- [Model Predictive Control for Signal Temporal Logic Specification](https://mlsanity.com/paper/1703.09563/) (2017)
- [Hierarchical Potential-based Reward Shaping from Task Specifications](https://mlsanity.com/paper/2110.02792/) (2021)
- [Reactive and Risk-Aware Control for Signal Temporal Logic](https://mlsanity.com/paper/2108.13490/) (2021)
- [A Scenario Approach to Risk-Aware Safety-Critical System Verification](https://mlsanity.com/paper/2203.02595/) (2022)
- [Robust Control for Signal Temporal Logic Specifications using Average Space Robustness](https://mlsanity.com/paper/1607.07019/) (2016)
- [Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications](https://mlsanity.com/paper/1903.06972/) (2019)
- [Prescribed-time convergence with input constraints: A control Lyapunov function based approach](https://mlsanity.com/paper/1903.06974/) (2019)
- [Risk of Stochastic Systems for Temporal Logic Specifications](https://mlsanity.com/paper/2205.14523/) (2022)

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

- [Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout](https://mlsanity.com/paper/2403.15826/) (2024)

## Abstract (arXiv)

In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives. We assume that the hard constraints encoding safety or mission-critical task objectives are expressed using Signal Temporal Logic (STL), while performance is quantified using standard cost functions on system trajectories. In order to prioritize the satisfaction of the hard STL constraints, we utilize the framework of control barrier functions (CBFs) and algorithmically obtain CBFs for STL objectives. We assume that the controllers are modeled using neural networks (NNs) and provide an optimization algorithm to learn the optimal parameters for the NN controller that optimize the performance at a user-specified robustness margin for the safety specifications. We use the formalism of risk measures to evaluate the risk incurred by the trade-off between robustness margin of the system and its performance. We demonstrate the efficacy of our approach on well-known difficult examples for nonlinear control such as a quad-rotor and a unicycle, where the mission objectives for each system include hard timing constraints and safety objectives.