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Found 9 papers, 2 papers with code
Control Barrier Function for Linearizable Systems with High Relative Degrees from Signal Temporal Logics: A Reference Governor Approach
We developed an explicit reference governor-guided control barrier function (ERG-guided CBF) method that enables the application of first-order CBFs to high-order linearizable systems.
Symbolic Perception Risk in Autonomous Driving
We develop a novel framework to assess the risk of misperception in a traffic sign classification task in the presence of exogenous noise.
Learning Signal Temporal Logic through Neural Network for Interpretable Classification
Machine learning techniques using neural networks have achieved promising success for time-series data classification.
Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications
This is achieved by decomposing an infeasible LTL formula into several reach-avoid sub-tasks with shorter horizons, which can be trained in a modular DRL architecture.
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula.
Safety-Critical Learning of Robot Control with Temporal Logic Specifications
Then, by applying a reward shaping technique, we develop a modular policy-gradient architecture exploiting the benefits of the automaton structure to decompose overall tasks and enhance the performance of learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safe exploration during the learning process using Exponential Control Barrier Functions (ECBFs) that generalize systems with high-order relative degrees; (4) to further improve the efficiency of exploration, we utilize the properties of LTL automata and ECBFs to propose a safe guiding process.
Fast Decomposition of Temporal Logic Specifications for Heterogeneous Teams
In this work, we focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently.
Reinforcement Learning With Temporal Logic Rewards
We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i. e., robustness degree.
Time Window Temporal Logic
This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications.
Formal Languages and Automata Theory Logic in Computer Science
