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George J. Pappas

72 accepted papers

2026

Air-Ground Collaboration for Language-Specified Missions in Unknown Environments (I)

Fernando Cladera, Zachary Ravichandran, Jason Hughes, Varun Murali, Carlos Nieto-Granda, M. Ani Hsieh +3
ICRA 2026poster

As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming si…

Cited by 0Scholar ↗
2026

Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison

David Snyder, Apurva Badithela, Nikolai Matni, George J. Pappas, Anirudha Majumdar, Masha Itkina +1
RSS 2026poster

Generalist robot manipulation policies are becoming increasingly capable, but are limited in evaluation to a small number of hardware rollouts. This strong resource constraint in real-world testing necessitates both more informative performance measures and reliable and efficient evaluation procedur…

Cited by 0Source ↗Scholar ↗
2026

Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning

Nicolo Dal Fabbro, Milad Mesbahi, Mendes Renato, João Sousa, George J. Pappas
ICRA 2026poster

We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy - and communication - efficient multi-agent reinforcement learning approach in which a central co…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

Hanwen Cao, George J. Pappas, Nikolay Atanasov
ICRA 2026poster

In this paper, we derive a new Kalman filter (KF) with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We view the unknown data association as a latent …

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Preventing Robotic Jailbreaking Via Multimodal Domain Adaptation

Francesco Marchiori, Rohan Sinha, Christopher George Agia, Alexander Robey, George J. Pappas, Mauro Conti +1
ICRA 2026poster

Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to jailbreaking attacks that bypass safety mechanisms and drive unsafe or physically harmful behaviors in the real world. Data-driven defenses such as jailbreak clas…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Safe Planning in Unknown Environments Using Conformalized Semantic Maps

David Smith Sundarsingh, Yifei Li, Tianji Tang, George J. Pappas, Nikolay Atanasov, Yiannis Kantaros
RA-L 2026

This paper addresses semantic planning problems in unknown environments under perceptual uncertainty. The environment contains multiple unknown semantically labeled regions or objects, and the robot must reach desired locations while maintaining class-dependent distances from them. We aim to compute

Cited by 1Source ↗Scholar ↗
2026

Safety Guardrails for LLM-Enabled Robots

Zachary Ravichandran, Alexander Robey, Vijay Kumar, George J. Pappas, Hamed Hassani
RA-L 2026

Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"

Cited by 30Source ↗code ↗Scholar ↗
2025

Adversarial Reasoning at Jailbreaking Time

Mahdi Sabbaghi, Paul Kassianik, George J. Pappas, Amin Karbasi, Hamed Hassani
ICML 2025poster

As large language models (LLMs) are becoming more capable and widespread, the study of their failure cases is becoming increasingly important. Recent advances in standardizing, measuring, and scaling test-time compute suggest new methodologies for optimizing models to achieve high performance on ha…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

CViT: Continuous Vision Transformer for Operator Learning

Sifan Wang, Jacob H Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris
ICLR 2025poster

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization

Sunay Joshi, Shayan Kiyani, George J. Pappas, Edgar Dobriban, Hamed Hassani
NeurIPS 2025poster

We consider the problem of conformal prediction under covariate shift. Given labeled data from a source domain and unlabeled data from a covariate shifted target domain, we seek to construct prediction sets with valid marginal coverage in the target domain. Most existing methods require estimating t…

Cited by 0Source ↗Scholar ↗
2025

Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models

Sima Noorani, Shayan Kiyani, George J. Pappas, Hamed Hassani
NeurIPS 2025poster

Uncertainty quantification (UQ) is essential for safe deployment of generative AI models such as large language models (LLMs), especially in high-stakes applications. Conformal prediction (CP) offers a principled uncertainty quantification framework, but classical methods focus on regression and cla…

Cited by 0Source ↗Scholar ↗
2025

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents

Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani
ICML 2025spotlight

A fundamental question in data-driven decision making is how to quantify the uncertainty of predictions to inform risk-sensitive downstream actions, as often required in domains such as medicine. We develop a decision-theoretic foundation linking prediction sets to risk-averse decision-making, addre…

Cited by 3Source ↗PDF ↗Scholar ↗
2025

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention

Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
CoRL 2025poster

Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or i…

Cited by 0Source ↗Scholar ↗
2025

Flying Quadrotors in Tight Formations Using Learning-Based Model Predictive Control

Kong Yao Chee, Pei-An Hsieh, George J. Pappas, M. Ani Hsieh
ICRA 2025

Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine learning tools can potentially be used to derive models that captur

Cited by 6Source ↗Scholar ↗
2025

SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments

Zachary Ravichandran, Varun Murali, Mariliza Tzes, George J. Pappas, Vijay Kumar
ICRA 2025

As robots become increasingly capable, users will want to describe high-level missions and have robots infer the relevant details. Because pre-built maps are difficult to obtain in many realistic settings, accomplishing such missions will require the robot to map and plan online. While many semantic

Cited by 25Source ↗code ↗Scholar ↗
2025

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands

Vasiliki Tassopoulou, Charis Stamouli, Haochang Shou, George J. Pappas, Christos Davatzikos
NeurIPS 2025poster

We introduce a novel conformal prediction framework for constructing conformal prediction bands with high probability around biomarker trajectories observed at subject-specific, randomly-timed follow-up visits. Existing conformal methods typically assume fixed time grids, limiting their applicabilit…

Cited by 0Source ↗code ↗Scholar ↗
2024

Adversarial Training Should Be Cast as a Non-Zero-Sum Game

Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher
ICLR 2024poster

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially chosen perturbations of data. Despite the promise of this approach, algorithms based on thi…

Cited by 15Source ↗PDF ↗Scholar ↗
2024

Conformal Prediction Regions for Time Series Using Linear Complementarity Programming

Matthew Cleaveland, Insup Lee, George J. Pappas, Lars Lindemann
AAAI 2024technical

Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, to obtain prediction regions over T time steps…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2024

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples

Thomas TCK Zhang, Bruce D Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni
ICML 2024poster

A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many sources. However, many practical domains involve data that are non-identically distributed across sources, and possibly statistically dependent within its source, vio…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag +6
NeurIPS 2024poster

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address. First, there is no clear…

Cited by 149Source ↗PDF ↗code ↗Scholar ↗
2024

Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss

Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
ICML 2024spotlight

In this work, we study statistical learning with dependent data and square loss in a hypothesis class with tail decay in Orlicz space: $\mathscr{F}\subset L_{\Psi_p}$. Our inquiry is motivated by the search for a sharp noise interaction term, or variance proxy, in learning with dependent (e.g. $\bet…

Cited by 9Source ↗PDF ↗Scholar ↗
2024

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

Arman Adibi, Nicolò Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas +2
AISTATS 2024poster

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in whi…

Cited by 14Source ↗PDF ↗Scholar ↗
2023

Active Collaborative Localization in Heterogeneous Robot Teams

Igor Spasojevic, Xu Liu, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
RSS 2023poster

Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) constrained aerial robots operating in perceptually-degraded GPS-denied environments. We can, however, actively increase t…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2023

Enhancing Sample Efficiency and Uncertainty Compensation in Learning-Based Model Predictive Control for Aerial Robots

Kong Yao Chee, Thales C. Silva, M. Ani Hsieh, George J. Pappas
IROS 2023poster

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on…

Cited by 7Source ↗Scholar ↗
2023

Graph Neural Networks for Multi-Robot Active Information Acquisition

Mariliza Tzes, Nikolaos Bousias, Evangelos Chatzipantazis, George J. Pappas
ICRA 2023poster

This paper addresses the Multi-Robot Active In-formation Acquisition (AIA) problem, where a team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Applications like target tracking, coverage and SLAM can be expressed in this fr…

Cited by 41Source ↗Scholar ↗
2023

Robust Localization of Aerial Vehicles via Active Control of Identical Ground Vehicles

Igor Spasojevic, Xu Liu, Ankit Prabhu, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
IROS 2023poster

This paper addresses the problem of active collaborative localization in heterogeneous robot teams with unknown data association. It involves positioning a small number of identical unmanned ground vehicles (UGVs) at desired positions so that an unmanned aerial vehicle (UAV) can, through unlabelled…

Cited by 4Source ↗Scholar ↗
2023

Safe Planning in Dynamic Environments Using Conformal Prediction

Lars Lindemann, Matthew Cleaveland, Gihyun Shim, George J. Pappas
RA-L 2023

We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive controller (MPC) that uses i) trajectory predictions of the dynamic environment, and ii) prediction regions quantifying the

Cited by 195Source ↗Scholar ↗
2023

The noise level in linear regression with dependent data

Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
NeurIPS 2023poster

We derive upper bounds for random design linear regression with dependent ($\beta$-mixing) data absent any realizability assumptions. In contrast to the strictly realizable martingale noise regime, no sharp \emph{instance-optimal} non-asymptotics are available in the literature. Up to constant fact…

Cited by 7Source ↗PDF ↗Scholar ↗
2022

Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds

Aritra Mitra, Arman Adibi, George J. Pappas, Hamed Hassani
NeurIPS 2022accept

We consider a linear stochastic bandit problem involving $M$ agents that can collaborate via a central server to minimize regret. A fraction $\alpha$ of these agents are adversarial and can act arbitrarily, leading to the following tension: while collaboration can potentially reduce regret, it can a…

Cited by 9Source ↗PDF ↗Scholar ↗
2022

Do deep networks transfer invariances across classes?

Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani +1
ICLR 2022poster

In order to generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply similarly to all classes, such as lighting and background changes for image classification. Neur…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2022

NOMAD: Nonlinear Manifold Decoders for Operator Learning

Jacob H Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
NeurIPS 2022accept

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, the…

Cited by 93Source ↗PDF ↗Scholar ↗
2022

Probabilistically Robust Learning: Balancing Average and Worst-case Performance

Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani
ICML 2022spotlight

Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder its applicability in safety-critical domains. These issues are often addressed by training against worst-case perturbat…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗
2022

Probable Domain Generalization via Quantile Risk Minimization

Cian Eastwood, Alexander Robey, Shashank Singh, Julius Von Kügelgen, Hamed Hassani, George J. Pappas +1
NeurIPS 2022accept

Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To achieve this, DG is commonly formulated as an average- or worst-case problem over the set of possible domains. However, pre…

Cited by 76Source ↗PDF ↗Scholar ↗
2022

Reactive Informative Planning for Mobile Manipulation Tasks under Sensing and Environmental Uncertainty

Mariliza Tzes, Vasileios Vasilopoulos, Yiannis Kantaros, George J. Pappas
ICRA 2022poster

In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tas…

Cited by 7Source ↗Scholar ↗
2021

Adversarial Robustness with Semi-Infinite Constrained Learning

Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro
NeurIPS 2021poster

Despite strong performance in numerous applications, the fragility of deep learning to input perturbations has raised serious questions about its use in safety-critical domains. While adversarial training can mitigate this issue in practice, state-of-the-art methods are increasingly application-dep…

Cited by 48Source ↗PDF ↗code ↗Scholar ↗
2021

Deep Reinforcement Learning for Active Target Tracking

Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
ICRA 2021poster

We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked with acquiring information about targets of interests using its on-board sensors. The classical challenges in this prob…

Cited by 8Source ↗Scholar ↗
2021

Distributed Sampling-based Planning for Non-Myopic Active Information Gathering

Mariliza Tzes, Yiannis Kantaros, George J. Pappas
IROS 2021poster

This paper addresses the problem of active information gathering for multi-robot systems. Specifically, we consider scenarios where robots are tasked with reducing uncertainty of dynamical hidden states evolving in complex environments. The majority of existing information gathering approaches are c…

Cited by 10Source ↗Scholar ↗
2021

Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani
NeurIPS 2021poster

We consider a standard federated learning (FL) setup where a group of clients periodically coordinate with a central server to train a statistical model. We develop a general algorithmic framework called FedLin to tackle some of the key challenges intrinsic to FL, namely objective heterogeneity, sys…

Cited by 188Source ↗PDF ↗Scholar ↗
2021

Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams

Xiaoyi Cai, Brent Schlotfeldt, Kasra Khosoussi, Nikolay Atanasov, George J. Pappas, Jonathan P. How
ICRA 2021poster

This paper considers the problem of planning trajectories for a team of sensor-equipped robots to reduce uncertainty about a dynamical process. Optimizing the trade-off between information gain and energy cost (e.g., control effort, distance travelled) is desirable but leads to a non-monotone object…

Cited by 22Source ↗Scholar ↗
2021

Reactive Planning for Mobile Manipulation Tasks in Unexplored Semantic Environments

Vasileios Vasilopoulos, Yiannis Kantaros, George J. Pappas, Daniel E. Koditschek
ICRA 2021poster

Complex manipulation tasks, such as rearrangement planning of numerous objects, are combinatorially hard problems. Existing algorithms either do not scale well or assume a great deal of prior knowledge about the environment, and few offer any rigorous guarantees. In this paper, we propose a novel hy…

Cited by 25Source ↗Scholar ↗
2021

Scalable Reinforcement Learning Policies for Multi-Agent Control

Christopher D. Hsu, Heejin Jeong, George J. Pappas, Pratik Chaudhari
IROS 2021poster

We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-o…

Cited by 42Source ↗code ↗Scholar ↗
2020

A Zeroth-Order Learning Algorithm for Ergodic Optimization of Wireless Systems with no Models and no Gradients

Dionysios S. Kalogerias, Mark Eisen, George J. Pappas, Alejandro Ribeiro
ICASSP 2020accepted

Optimal resource allocation in real-world wireless systems is rather challenging, not only due to the unavailability of accurate statistical channel models, but also because expressions of maximal or achievable information rates are most often unknown, or not adequately precise. Under a modular stoc…

Cited by 0Source ↗Scholar ↗
2020

Adaptive Partitioning for Coordinated Multi-agent Perimeter Defense

Douglas G. Macharet, Austin K. Chen, Daigo Shishika, George J. Pappas, Vijay Kumar
IROS 2020poster

Multi-Robot Systems have been recently employed in different applications and have advantages over single-robot systems, such as increased robustness and task performance efficiency. We consider such assemblies specifically in the scenario of perimeter defense, where the task is to defend a circular…

Cited by 32Source ↗Scholar ↗
2020

Better Safe Than Sorry: Risk-Aware Nonlinear Bayesian Estimation

Dionysios S. Kalogerias, Luiz F. O. Chamon, George J. Pappas, Alejandro Ribeiro
ICASSP 2020accepted

Despite the simplicity and intuitive interpretation of minimum mean squared error (MMSE) estimators, their effectiveness in certain scenarios is questionable. Indeed, minimizing squared errors on average does not provide any form of stability, as the volatility of the estimation error is left uncons…

Cited by 0Source ↗Scholar ↗
2020

Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning

Lifeng Zhou, Vasileios Tzoumas, George J. Pappas, Pratap Tokekar
ICRA 2020poster

We aim to guard swarm-robotics applications against denial-of-service (DoS) attacks that result in withdrawals of robots. We focus on applications requiring the selection of actions for each robot, among a set of available ones, e.g., which trajectory to follow. Such applications are central in larg…

Cited by 57Source ↗Scholar ↗
2020

Information Theoretic Active Exploration in Signed Distance Fields

Kelsey Saulnier, Nikolay Atanasov, George J. Pappas, Vijay Kumar
ICRA 2020poster

This paper focuses on exploration and occupancy mapping of unknown environments using a mobile robot. While a truncated signed distance field (TSDF) is a popular, efficient, and highly accurate representation of occupancy, few works have considered optimizing robot sensing trajectories for autonomou…

Cited by 37Source ↗Scholar ↗
2020

Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback

Vasileios Vasilopoulos, Georgios Pavlakos, Sean L. Bowman, J. Diego Caporale, Kostas Daniilidis, George J. Pappas +1
RA-L 2020

This letter presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combi

Cited by 34Source ↗Scholar ↗
2020

Reactive Temporal Logic Planning for Multiple Robots in Unknown Environments

Yiannis Kantaros, Matthew Malencia, Vijay Kumar, George J. Pappas
ICRA 2020poster

This paper proposes a new reactive mission planning algorithm for multiple robots that operate in unknown environments. The robots are equipped with individual sensors that allow them to collectively learn and continuously update a map of the unknown environment. The goal of the robots is to accompl…

Cited by 46Source ↗Scholar ↗
2019

Asymptotically Optimal Planning for Non-Myopic Multi-Robot Information Gathering

Yiannis Kantaros, Brent Schlotfeldt, Nikolay Atanasov, George J. Pappas
RSS 2019poster

This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others.…

Cited by 68Source ↗PDF ↗Scholar ↗
2019

Learning Q-network for Active Information Acquisition

Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
IROS 2019poster

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors. The classic challenges in the informat…

Cited by 21Source ↗Scholar ↗
2019

Maximum Information Bounds for Planning Active Sensing Trajectories

Brent Schlotfeldt, Nikolay Atanasov, George J. Pappas
IROS 2019poster

This paper considers the problem of planning trajectories for robots equipped with sensors whose task is to track an evolving target process in the world. We focus on processes which can be represented by a Gaussian random variable, which is known to reduce the general stochastic information acquisi…

Cited by 14Source ↗Scholar ↗
2018

Anytime Planning for Decentralized Multirobot Active Information Gathering

Brent Schlotfeldt, Dinesh Thakur, Nikolay Atanasov, Vijay Kumar, George J. Pappas
RA-L 2018

This letter considers the problem of reducing uncertainty about a physical process of interest by designing sensing trajectories for a team of robots. This active information gathering problem has applications in environmental monitoring, search and rescue, and security and surveillance. Our previou

Cited by 126Source ↗Scholar ↗
2018

Learning Statistically Accurate Resource Allocations in Non-Stationary Wireless Systems

Mark Eisen, Konstantinos Gatsis, George J. Pappas, Alejandro Ribeiro
ICASSP 2018accepted

This paper considers the resource allocation problem in wireless systems over an unknown time-varying non-stationary channel. The goal is to maximize a utility function, such as a capacity function, over a set of wireless nodes while satisfying a set of resource constraints. To bypass the need for a…

Cited by 0Source ↗Scholar ↗
2018

Resilient Active Information Gathering with Mobile Robots

Brent Schlotfeldt, Vasileios Tzoumas, Dinesh Thakur, George J. Pappas
IROS 2018poster

Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and th…

Cited by 36Source ↗Scholar ↗
2017

Probabilistic data association for semantic SLAM

Sean L. Bowman, Nikolay Atanasov, Kostas Daniilidis, George J. Pappas
ICRA 2017poster

Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is ofte…

Cited by 605Source ↗Scholar ↗
2015

Decentralized active information acquisition: Theory and application to multi-robot SLAM

Nikolay Atanasov, Jerome Le Ny, Kostas Daniilidis, George J. Pappas
ICRA 2015poster

This paper addresses the problem of controlling mobile sensing systems to improve the accuracy and efficiency of gathering information autonomously. It applies to scenarios such as environmental monitoring, search and rescue, surveillance and reconnaissance, and simultaneous localization and mapping…

Cited by 238Source ↗Scholar ↗