Temporal logic control of a Van der Pol oscillator using a piecewise-affine abstraction

Software corresponding to the case study of a forced, stochastically perturbed Van der Pol oscillator as described in the paper "Temporal logic control of nonlinear stochastic systems using a piecewise-affine abstraction". In this paper we develop a method to automatically synthesize controllers for continuous-state nonlinear stochastic systems, while giving guarantees on the probability of satisfying (infinite-horizon) temporal logic specifications. Such methods crucially depend on abstractions with a quantified accuracy. For this similarity quantification, approximate stochastic simulation relations are often used. To handle the nonlinearity of the system effectively, we use finite-state abstractions based on piecewise-affine approximations together with tailored simulation relations that leverage the local affine structure. In the end, we synthesize a robust controller for a nonlinear stochastic Van der Pol oscillator

Reinforcement Learning for Robot Motion Planning Facilitated by Implicit Behavior Cloning and Dynamic Movement Primitive (IBC-DMP RL)

This dataset contains the programs to train and test the implicit behavior cloning (IBC) dynamic movement primitive (DMP) reinforcement learning (RL) agent for robot motion planning. It is associated with an under-reviewed journal paper with the same title. See ReadMe.md in the zip file for details

Dataset of Human Hand Motion Planning

This dataset contains 544 human hand motion trajectories in a point-to-point reaching experiment. The purpose of this dataset is to provide human demonstrations for imitation-learning- and reinforcement-learning -based robot motion planning. Refer to 'ReadMe.md' for the details about the format and usage of the dataset

SySCoRe (ARCH 2022)

Synthesis via Stochastic Coupling Relations (SysCoRe) for stochastic continuous-state systems

Decentralized Optimal Coverage Control for Constant-Speed Unicycle Multi-Agent Systems

This dataset contains the program code for the following publication: Liu, Qingchen, Zengjie Zhang, Nhan Khanh Le, Jiahu Qin, Fangzhou Liu, and Sandra Hirche. "Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems." IEEE Transactions on Automation Science and Engineering (2024). It can be used for coverage control of fixed-wing drones

SySCoRe: Synthesis via Stochastic Coupling Relations

SySCoRe is a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction. To achieve this, SySCoRe first generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linear in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several benchmarks

SySCoRe Repeatability Package (ARCH 2023 Category Report Case Study)

Synthesis via Stochastic Coupling Relations (SySCoRe) for stochastic continuous-state systems