Temporal Logic

Temporal logic (TL) is a formal system for specifying and reasoning about properties that change over time, primarily used to define complex objectives for autonomous systems. Current research focuses on efficiently synthesizing controllers and plans that satisfy TL specifications, often employing techniques like reinforcement learning, large language models (LLMs) for natural language specification translation, and automata-based methods for verification and synthesis. These advancements are significantly impacting robotics, autonomous driving, and other domains requiring safe and reliable control under complex temporal constraints, enabling more robust and adaptable systems.

Papers

April 25, 2022

Planning and Control of Multi-Robot-Object Systems under Temporal Logic Tasks and Uncertain Dynamics
Christos K. Verginis, Yiannis Kantaros, Dimos V. Dimarogonas
External Control Task Planning Multi Robot System Temporal Logic Multiple Robot Safe Robot Navigation Uncertain Dynamic Temporal Logic Task Obstacle Cluttered Environment

April 21, 2022

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning
Giuseppe De Giacomo, Marco Favorito, Francesco Fuggitti
Task Planning Temporal Logic Temporal Goal Formal Method Reachability Objective

April 18, 2022

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
Adam Pacheck, Steven James, George Konidaris, Hadas Kress-Gazit
Robot Person Temporal Logic Third Position Repair Automatic Coding Baxter Robot Reactive Programming

April 15, 2022

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network
Gang Chen, Yu Lu, Rong Su, Zhaodan Kong
Temporal Logic Signal Temporal Logic Fault Diagnosis Rolling Bearing Machinery Fault Interpretable Fault

April 11, 2022

Automated Task Updates of Temporal Logic Specifications for Heterogeneous Robots
Amy Fang, Hadas Kress-Gazit
Temporal Logic Robotic Task Complex Task Heterogeneous Robot Multi Robot Task Task Automation

April 7, 2022

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies
Duo Xu, Faramarz Fekri
Deep Reinforcement Learning New Framework Temporal Logic Hierarchical Reinforcement Learning Inductive Logic Programming Underlying Causal Transition Model

April 1, 2022

To Explore or Not to Explore: Regret-Based LTL Planning in Partially-Known Environments
Jianing Zhao, Keyi Zhu, Mingyang Feng, Xiang Yin
Temporal Logic Optimal Planning

March 9, 2022

Computing unsatisfiable cores for LTLf specifications
Marco Roveri, Claudio Di Ciccio, Chiara Di Francescomarino, Chiara Ghidini
Temporal Logic Digital Computing Satisfiability Problem LTLf Specification

March 8, 2022

Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping
Ningyuan Zhang, Wenliang Liu, Calin Belta
Reinforcement Learning Temporal Logic Reward Shaping Stochastic Game Heterogeneous Team Finite State Automaton Distributed Control

February 9, 2022

Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences
Hao Wang, Haoyuan He, Weiwei Shang, Zhen Kan
Motion Planning Temporal Logic Manipulation Task Motion Primitive Dynamic Movement Primitive

January 28, 2022

Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
Mingyu Cai, Erfan Aasi, Calin Belta, Cristian-Ioan Vasile
Deep Reinforcement Learning Policy Gradient Temporal Logic Reward Model Navigation Task Continuous Control Cluttered Environment

December 28, 2021

Time-Incremental Learning from Data Using Temporal Logics
Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta
Raw Data Temporal Logic Abnormal Signal Signal Temporal Logic Incremental Learning Framework Dynamic Weight

December 20, 2021

Demonstration Informed Specification Search
Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman, Sanjit A. Seshia
Temporal Logic Task Specification Temporal Task Specification Mining

November 24, 2021

On the (In)Tractability of Reinforcement Learning for LTL Objectives
Cambridge Yang, Michael Littman, Michael Carbin
Reinforcement Learning Markov Decision Process Temporal Logic Moral Tractability

November 18, 2021

Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines
Xuejing Zheng, Chao Yu, Chen Chen, Jianye Hao, Hankz Hankui Zhuo
Temporal Logic Continuous Learning Lifelong Reinforcement Learning Reward Machine Temporal Logic Formula

November 9, 2021

Self-checking Logical Agents
Stefania Costantini
Temporal Logic Agent Based Temporal Logical Rule Logical Agent

November 7, 2021

Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator
Homer Walke, Daniel Ritter, Carl Trimbach, Michael Littman
Neural Operator Temporal Logic Neural Scaling Law

Temporal Logic

Papers

Planning and Control of Multi-Robot-Object Systems under Temporal Logic Tasks and Uncertain Dynamics

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Automated Task Updates of Temporal Logic Specifications for Heterogeneous Robots

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies

To Explore or Not to Explore: Regret-Based LTL Planning in Partially-Known Environments

Computing unsatisfiable cores for LTLf specifications

Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping

Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks with User Preferences

Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications

Time-Incremental Learning from Data Using Temporal Logics

Demonstration Informed Specification Search

On the (In)Tractability of Reinforcement Learning for LTL Objectives

Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines

Self-checking Logical Agents

Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator