An Operator-Based Approach to STL
- URL: http://arxiv.org/abs/2605.28092v1
- Date: Wed, 27 May 2026 07:47:45 GMT
- Title: An Operator-Based Approach to STL
- Abstract summary: We propose a novel approach to Signal Temporal Logic (STL) based on an operator acting on reachability value functions.<n>This constitutes a new theoretical framework for handling complex multi-nested formulae.<n>In contrast to focusing on the design of STL-based reachability functions, we develop operator-based nesting rules.
- Score: 10.103844123344057
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Signal Temporal Logic (STL), has recently seen extensive development, owing to its rich expressivenes for autonomous planning and control. Nevertheless, existing verification and control synthesis methods are limited with respect to the complexity and degree of nesting of the formulae. In this work, we propose a novel approach to STL based on an operator acting on reachability value functions. This constitutes a new theoretical framework for handling complex multi-nested formulae while at the same time providing tools for on-line control synthesis. In contrast to focusing on the design of STL-based reachability (or control barrier) functions, we develop operator-based nesting rules directly. Our method's expressiveness is demonstrated both theoretically, where necessary and sufficient conditions for STL formula satisfaction are extracted, as well as in simulations with complex fragments.
Related papers
- Correct-by-Construction Behavior Tree Synthesis from Signal Temporal Logic Specifications with Application to Robotic Missions [5.524204636266904]
Behavior Trees (BTs) are widely adopted for complex task execution in robotics.<n>This letter synthesizes correct-by-construction BTs from Signal Temporal Logic (STL) specifications.
arXiv Detail & Related papers (2026-07-21T05:43:19Z) - On Tackling Complex Tasks with Reward Machines and Signal Temporal Logics [42.04219310123725]
We propose a Reinforcement Learning (RL) based control design framework for handling complex tasks.<n>The approach extends the concept of Reward Machines (RM) with Signal Temporal Logic (STL) formulas that can be used for event generation.
arXiv Detail & Related papers (2026-04-09T13:39:12Z) - Zero-Shot Instruction Following in RL via Structured LTL Representations [50.41415009303967]
We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.<n>In this setting, linear temporal logic has recently been adopted as a powerful framework for specifying structured, temporally extended tasks.<n>While existing approaches successfully train generalist policies, they often struggle to effectively capture the rich logical and temporal structure inherent in specifications.
arXiv Detail & Related papers (2026-02-15T23:22:50Z) - Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions [61.479946958462754]
We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy.<n>We present a novel task embedding technique leveraging a new generation of semantic translations-to-automata.
arXiv Detail & Related papers (2026-02-06T14:46:27Z) - Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version) [49.462399222747024]
We propose a novel framework for the logical specification of non-Markovian rewards in Decision Processes (MDPs) with large state spaces.<n>Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT)<n>We introduce a method based on reward machines and Hindsight Experience Replay (HER) to translate first-order logic specifications and address reward sparsity.
arXiv Detail & Related papers (2026-02-05T22:11:28Z) - Zero-Shot Instruction Following in RL via Structured LTL Representations [54.08661695738909]
Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents.<n>Recent work has shown that interpreting instructions as finite automata, which can be seen as high-level programs monitoring task progress, enables learning a single generalist policy capable of executing arbitrary instructions at test time.<n>We propose a novel approach to learning a multi-task policy for following arbitrary instructions that addresses this shortcoming.
arXiv Detail & Related papers (2025-12-02T10:44:51Z) - Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
arXiv Detail & Related papers (2025-10-02T15:15:50Z) - Verified Lifting of Deep learning Operators [9.48599997951339]
This work introduces a novel framework for the verified lifting of deep learning operators.<n>It synthesizes high-level mathematical formulas from low-level implementations.<n>This framework bridges the gap between low-level implementations and high-level abstractions.
arXiv Detail & Related papers (2024-12-30T14:57:32Z) - CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning [1.433758865948252]
We introduce CoT-TL, a data-efficient in-context learning framework for translating natural language specifications into representations.<n>CoT-TL achieves state-of-the-art accuracy across three diverse datasets in low-data scenarios.<n>We incorporate CoT-TL's practicality into a QuadCopter for multi-step drone planning based on natural language instructions.
arXiv Detail & Related papers (2024-10-21T17:10:43Z) - TLINet: Differentiable Neural Network Temporal Logic Inference [10.36033062385604]
This paper introduces TLINet, a neural-symbolic framework for learning STL formulas.
In contrast to existing approaches, we introduce approximation methods for max operator designed specifically for temporal logic-based gradient techniques.
Our framework not only learns the structure but also the parameters of STL formulas, allowing flexible combinations of operators and various logical structures.
arXiv Detail & Related papers (2024-05-03T16:38:14Z) - Certified Reinforcement Learning with Logic Guidance [78.2286146954051]
We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs)
The algorithm is guaranteed to synthesise a control policy whose traces satisfy the specification with maximal probability.
arXiv Detail & Related papers (2019-02-02T20:09:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.