Search Results for author: Yiannis Kantaros

Found 14 papers, 2 papers with code

Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction

no code implementations23 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.

Conformal Prediction Uncertainty Quantification

Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis

no code implementations18 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.

Bayesian Inference Reinforcement Learning (RL)

Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks

no code implementations17 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.

Robot Navigation

Conformal Temporal Logic Planning using Large Language Models

no code implementations18 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.

Conformal Prediction Motion Planning

Neural Lyapunov Control for Discrete-Time Systems

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.

Targeted Adversarial Attacks against Neural Network Trajectory Predictors

no code implementations8 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.

Adversarial Attack Trajectory Forecasting

Accelerated Reinforcement Learning for Temporal Logic Control Objectives

no code implementations9 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

Planning and Control of Multi-Robot-Object Systems under Temporal Logic Tasks and Uncertain Dynamics

no code implementations25 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.

Robot Navigation

Model-Based Robust Adaptive Semantic Segmentation

no code implementations29 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.

Autonomous Vehicles Image Segmentation +2

A Temporal Logic-Based Hierarchical Network Connectivity Controller

no code implementations1 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.

Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems

no code implementations23 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.

Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

1 code implementation11 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.

Decision Making Decision Making Under Uncertainty +4

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