# Back-propagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods

@inproceedings{Leung2021BackpropagationTS, title={Back-propagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods}, author={Karen Leung and Nikos Ar{\'e}chiga and Marco Pavone}, booktitle={WAFR}, year={2021} }

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics…

## 17 Citations

Signal Temporal Logic Synthesis as Probabilistic Inference

- Computer Science2021 IEEE International Conference on Robotics and Automation (ICRA)
- 2021

The notion of random STL (RSTL), which extends deterministic STL with random predicates, is introduced, which leads to a synthesis-as-inference approach and allows for differentiable, gradient-based synthesis while extending the class of possible uncertain semantics.

Robust Counterexample-guided Optimization for Planning from Differentiable Temporal Logic

- Computer ScienceArXiv
- 2022

This paper presents an algorithm for finding robust plans that satisfy STL specifications, using automatically differentiable temporal logic to iteratively optimize its plan in response to counterexamples found during the falsification process.

Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications

- Computer ScienceArXiv
- 2022

This work proposes a semi-supervised controller synthesis technique that is attuned to human-like behaviors while satisfying desired STL specifications and demonstrates that having imitationbased regularization leads to higher qualitative and quantitative performance compared to optimizing an STL objective only as done in prior work.

Multi-Agent Motion Planning From Signal Temporal Logic Specifications

- Computer ScienceIEEE Robotics and Automation Letters
- 2022

Results show that the method based on timed waypoints supports multi-agent planning from complex specification over long planning horizons, and significantly outperforms state-of-the-art abstraction-based and MPC-based motion planning methods.

Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints

- Computer ScienceIEEE Control Systems Letters
- 2022

We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal…

Model-Based Safe Policy Search from Signal Temporal Logic Specifications Using Recurrent Neural Networks

- Computer ScienceArXiv
- 2021

This work proposes a policy search approach to learn controllers from speciﬁcations given as Signal Temporal Logic (STL) formulae, and uses control barrier functions (CBFs) with the learned model to improve the safety of the system.

Learning A Risk-Aware Trajectory Planner From Demonstrations Using Logic Monitor

- Computer ScienceCoRL
- 2021

This work uses a logic monitor that keeps track of the environmental agents’ behaviors and provides a risk metric that the controlled agent can incorporate during planning, and introduces LogicRiskNet, a learning structure that can be constructed from temporal logic formulas describing rules governing a safe agent’s behaviors.

Learning Provably Robust Motion Planners Using Funnel Libraries

- Computer ScienceArXiv
- 2021

The ability of the approach to provide strong guarantees on two simulated examples: navigation of an autonomous vehicle under external disturbances on a five-lane highway with multiple vehicles, and navigation of a drone across an obstacle field in the presence of wind disturbances is demonstrated.

Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

- Computer ScienceIEEE Robotics and Automation Letters
- 2021

A framework based on generative adversarial networks that uses tools from formal methods, namely signal temporal logic and syntax trees, allows us to leverage information on rule obedience as features in neural networks and improves prediction accuracy without biasing towards lawful behavior.

Knowledge-Integrated Informed AI for National Security

- Computer ScienceArXiv
- 2022

Findings are shared from a thorough exploration of AI approaches that exploit data as well as principled and/or practical knowledge, which are referred to as “knowledge-integrated informed AI” and how the advantages stand to benefit the national security domain.

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