no code implementations • 23 Feb 2024 • Jun Wang, Guocheng He, Yiannis Kantaros
Several recent works have addressed similar planning problems by leveraging pre-trained Large Language Models (LLMs) to design effective multi-robot plans.
no code implementations • 18 Dec 2023 • Rohan Mitta, Hosein Hasanbeig, Jun Wang, Daniel Kroening, Yiannis Kantaros, Alessandro Abate
This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during learning.
no code implementations • 28 Nov 2023 • Jun Wang, Hosein Hasanbeig, Kaiyuan Tan, Zihe Sun, Yiannis Kantaros
We consider robots with unknown stochastic dynamics operating in environments with unknown geometric structure.
no code implementations • 17 Nov 2023 • Jun Wang, Haojun Chen, Zihe Sun, Yiannis Kantaros
To the best of our knowledge, this is the first work that designs verified temporal compositions of NN controllers for unknown and stochastic systems.
no code implementations • 18 Sep 2023 • Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros
To formally define the overarching mission, we leverage Linear Temporal Logic (LTL) defined over atomic predicates modeling these NL-based sub-tasks.
1 code implementation • NeurIPS 2023 • Junlin Wu, Andrew Clark, Yiannis Kantaros, Yevgeniy Vorobeychik
However, finding Lyapunov functions for general nonlinear systems is a challenging task.
no code implementations • 27 Apr 2023 • Ramneet Kaur, Yiannis Kantaros, Wenwen Si, James Weimer, Insup Lee
Nevertheless, DNN models have proven to be vulnerable to adversarial digital and physical attacks.
no code implementations • 8 Dec 2022 • Kaiyuan Tan, Jun Wang, Yiannis Kantaros
To bridge this gap, in this paper, we propose a targeted adversarial attack against DNN models for trajectory forecasting tasks.
no code implementations • 9 May 2022 • Yiannis Kantaros
To address this problem, we propose a novel accelerated model-based reinforcement learning (RL) algorithm for LTL control objectives that is capable of learning control policies significantly faster than related approaches.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 25 Apr 2022 • Christos K. Verginis, Yiannis Kantaros, Dimos V. Dimarogonas
We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties.
no code implementations • 29 Sep 2021 • Jun Wang, Yiannis Kantaros
To mitigate this challenge, in this paper, we propose model-based robust adaptive training algorithm (MRTAdapt), a new training algorithm to enhance the robustness of DNN-based semantic segmentation methods against natural variations that leverages model-based robust training algorithms and generative adversarial networks.
no code implementations • 1 Sep 2020 • Hans Riess, Yiannis Kantaros, George Pappas, Robert Ghrist
We show that these constraints along with the requirement of propagating information in the network can be captured by a Linear Temporal Logic (LTL) framework.
no code implementations • 23 Feb 2020 • Yiannis Kantaros, Taylor Carpenter, Kaustubh Sridhar, Yahan Yang, Insup Lee, James Weimer
To highlight this, we demonstrate the efficiency of the proposed detector on ImageNet, a task that is computationally challenging for the majority of relevant defenses, and on physically attacked traffic signs that may be encountered in real-time autonomy applications.
1 code implementation • 11 Sep 2019 • Mohammadhosein Hasanbeig, Yiannis Kantaros, Alessandro Abate, Daniel Kroening, George J. Pappas, Insup Lee
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology.