Path planning from STL (Signal Temporal Logic) specifications
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Updated
Nov 16, 2022 - Python
Path planning from STL (Signal Temporal Logic) specifications
A library for efficiently working with Signal Temporal Logic (STL) and its quantitative semantics. Has Python bindings!
Online Signal Temporal Logic (STL) Monte-Carlo Tree Search for Guided Imitation Learning
An Efficient Convex Optimization-based Framework for Signal Temporal Logic (STL) Specifications
[RA-L2024/ICRA2025] Official implementation for paper "Diverse Controllable Diffusion Policy with Signal Temporal Logic."
[ICRA2024] A differentiable robot learning framework for task specifications and controller synthesis.
Turn STL formulas into maps and planed paths, control robots with DRL controllers.
Differentiable Symbolic Specification
This is the official repository for the Applied Sciences paper "Mining Road Traffic Rules with Signal Temporal Logic and Grammatical Evolution".
This is the official repository for the Evolutionary Computation paper "BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data".
Splitting signal temporal logic (STL) specifications in time
Cyber-Physical Sysems course Exam: PID, Reinforcement Learning and Temporal Logic
Lightweight Python package for doing operations concerning Assume-Guarantee (A/G) Contracts and parsing, deriving constraints from, and solving Signal Temporal Logic. Built at DesCyPhy Lab, USC
In this repository you can find the code necessary for the required exercise on learning STL formulae.
Intention-aware control using stochastic expansion methods
[RA-L2023/ICRA2024] A differentiable robot learning framework for task specifications and controller synthesis.
This repository is for the artifact evaluation of the paper "Online Causation Monitoring of Signal Temporal Logic", accepted by CAV 2023.
Synthesizing a trace that satisfies a given STL formula [CAV'24]
This repository is for the ACC 2023 conference paper titled "Statistical Verification of Traffic Systems with Expected Differential Privacy
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