3 citations · 6 across the 9 of their papers we have counts for
9 papers
Feasibility-aware Learning of Robust Temporal Logic Controllers using BarrierNet
Wenliang Liu, Shuo Liu, Wei Xiao +1
Control Barrier Functions (CBFs) have been used to enforce safety and task specifications expressed in Signal Temporal Logic (STL). However, existing CBF-STL approaches typically r…
Capability Augmentation for Heterogeneous Dynamic Teaming with Temporal Logic Tasks
Carter Berlind, Wenliang Liu, Alyssa Pierson +1
This paper considers how heterogeneous multi-agent teams can leverage their different capabilities to mutually improve individual agent performance. We present Capability-Augmentin…
Interpretable Imitation Learning via Generative Adversarial STL Inference and Control
Wenliang Liu, Danyang Li, Erfan Aasi +3
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods…
Learning Robust and Correct Controllers from Signal Temporal Logic Specifications Using BarrierNet
Wenliang Liu, Wei Xiao, Calin Belta
In this paper, we consider the problem of learning a neural network controller for a system required to satisfy a Signal Temporal Logic (STL) specification. We exploit STL quantita…
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
Wenliang Liu, Kevin Leahy, Zachary Serlin +1
In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under c…
Robust Multi-Agent Coordination from CaTL+ Specifications
Wenliang Liu, Kevin Leahy, Zachary Serlin +1
We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements. Capability Temporal Logic (CaTL) was recently proposed to…
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping
Ningyuan Zhang, Wenliang Liu, Calin Belta
We present a computational framework for synthesis of distributed control strategies for a heterogeneous team of robots in a partially observable environment. The goal is to cooper…
Safe Model-based Control from Signal Temporal Logic Specifications Using Recurrent Neural Networks
Wenliang Liu, Mirai Nishioka, Calin Belta
We propose a policy search approach to learn controllers from specifications given as Signal Temporal Logic (STL) formulae. The system model, which is unknown but assumed to be an…
Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints
Wenliang Liu, Noushin Mehdipour, Calin Belta
We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications give…