no code implementations • 29 Mar 2024 • Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao
We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds.
no code implementations • 15 Feb 2024 • Wenliang Liu, Danyang Li, Erfan Aasi, Roberto Tron, Calin Belta
Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations.
no code implementations • 12 Apr 2023 • 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.
no code implementations • 30 Nov 2022 • Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta
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 complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications.
no code implementations • 4 Oct 2022 • Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta
We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements.
no code implementations • 8 Mar 2022 • 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.
no code implementations • 29 Mar 2021 • Wenliang Liu, Mirai Nishioka, Calin Belta
To capture the history dependency of STL specifications, we use a recurrent neural network (RNN) to implement the control policy.
no code implementations • 24 Sep 2020 • 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 given as Signal Temporal Logic (STL) formulae.