Jerome Le Ny

Rabih Salhab, Jerome Le Ny, Roland P. Malhamé

We consider a dynamic collective choice problem where a large number of players are cooperatively choosing between multiple destinations while being influenced by the behavior of the group. For example, in a robotic swarm exploring a new environment, a robot might have to choose between multiple sites to visit, but at the same time it should remain close to the group to achieve some coordinated tasks. We show that to find a social optimum for our problem, one needs to solve a set of Linear Quadratic Regulator problems, whose number increases exponentially with the size of the population. Alternatively, we develop via the Mean Field Games methodology a set of decentralized strategies that are independent of the size of the population. When the number of agents is sufficiently large, these strategies qualify as approximately socially optimal. To compute the approximate social optimum, each player needs to know its own state and the statistical distributions of the players' initial states and problem parameters. Finally, we give a numerical example where the cooperative and noncooperative cases have opposite behaviors. Whereas in the former the size of the majority increases with the social effect, in the latter, the existence of a majority is disadvantaged.

Rabih Salhab, Roland P. Malhamé, Jerome Le Ny

Inspired by successful biological collective decision mechanisms such as honey bees searching for a new colony or the collective navigation of fish schools, we consider a mean field games (MFG)-like scenario where a large number of agents have to make a choice among a set of different potential target destinations. Each individual both influences and is influenced by the group's decision, as well as the mean trajectory of all the agents. The model can be interpreted as a stylized version of opinion crystallization in an election for example. The agents' biases are dictated first by their initial spatial position and, in a subsequent generalization of the model, by a combination of initial position and a priori individual preference. The agents have linear dynamics and are coupled through a modified form of quadratic cost. Fixed point based finite population equilibrium conditions are identified and associated existence conditions are established. In general multiple equilibria may exist and the agents need to know all initial conditions to compute them precisely. However, as the number of agents increases sufficiently, we show that 1) the computed fixed point equilibria qualify as epsilon Nash equilibria, 2) agents no longer require all initial conditions to compute the equilibria but rather can do so based on a representative probability distribution of these conditions now viewed as random variables. Numerical results are reported.

Jerome Le Ny

Real-time information processing applications such as those enabling a more intelligent infrastructure are increasingly focused on analyzing privacy-sensitive data obtained from individuals. To produce accurate statistics about the habits of a population of users of a system, this data might need to be processed through model-based estimators. Moreover, models of population dynamics, originating for example from epidemiology or the social sciences, are often necessarily nonlinear. Motivated by these trends, this paper presents an approach to design nonlinear privacy-preserving model-based observers, relying on additive input or output noise to give differential privacy guarantees to the individuals providing the input data. For the case of output perturbation, contraction analysis allows us to design convergent observers as well as set the level of privacy-preserving noise appropriately. Two examples illustrate the approach: estimating the edge formation probabilities in a dynamic social network, and syndromic surveillance relying on an epidemiological model.

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

Kesav Kaza, Jerome Le Ny, Aditya Mahajan

We consider the problem of optimal decision referrals in human-automation teams performing binary classification tasks. The automation, which includes a pre-trained classifier, observes data for a batch of independent tasks, analyzes them, and may refer a subset of tasks to a human operator for fresh and final analysis. Our key modeling assumption is that human performance degrades with task load. We model the problem of choosing which tasks to refer as a stochastic optimization problem and show that, for a given task load, it is optimal to myopically refer tasks that yield the largest reduction in expected cost, conditional on the observed data. This provides a ranking scheme and a policy to determine the optimal set of tasks for referral. We evaluate this policy against a baseline through an experimental study with human participants. Using a radar screen simulator, participants made binary target classification decisions under time constraint. They were guided by a decision rule provided to them, but were still prone to errors under time pressure. An initial experiment estimated human performance model parameters, while a second experiment compared two referral policies. Results show statistically significant gains for the proposed optimal referral policy over a blind policy that determines referrals using the automation and human-performance models but not based on the observed data.

Mohammed Ayman Shalaby, Charles Champagne Cossette, Jerome Le Ny et al.

Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.

Justin Cano, Jerome Le Ny

In robotic networks relying on noisy range measurements between agents for cooperative localization, the achievable positioning accuracy strongly strongly depends on the network geometry. This motivates the problem of planning robot trajectories in such multi-robot systems in a way that maintains high localization accuracy. We present potential-based planning methods, where localizability potentials are introduced to characterize the quality of the network geometry for cooperative position estimation. These potentials are based on Cramer Rao Lower Bounds (CRLB) and provide a theoretical lower bound on the error covariance achievable by any unbiased position estimator. In the process, we establish connections between CRLBs and the theory of graph rigidity, which has been previously used to plan the motion of robotic networks. We develop decentralized deployment algorithms appropriate for large networks, and we use equality-constrained CRLBs to extend the concept of localizability to scenarios where additional information about the relative positions of the ranging sensors is known. We illustrate the resulting robot deployment methodology through simulated examples and an experiment.

Mohammed Shalaby, Charles Champagne Cossette, Jerome Le Ny et al.

It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.

Jerome Le Ny, Simon Chauvière

When nodes in a mobile network use relative noisy measurements with respect to their neighbors to estimate their positions, the overall connectivity and geometry of the measurement network has a critical influence on the achievable localization accuracy. This paper considers the problem of deploying a mobile robotic network implementing a cooperative localization scheme based on range measurements only, while attempting to maintain a network geometry that is favorable to estimating the robots' positions with high accuracy. The quality of the network geometry is measured by a "localizability" function serving as potential field for robot motion planning. This function is built from the Cramér-Rao bound, which provides for a given geometry a lower bound on the covariance matrix achievable by any unbiased position estimator that the robots might implement using their relative measurements. We describe gradient descent-based motion planners for the robots that attempt to optimize or constrain different variations of the network's localizability function, and discuss ways of implementing these controllers in a distributed manner. Finally, the paper also establishes formal connections between our statistical point of view and maintaining a form of weighted rigidity for the graph capturing the relative range measurements.

Kwassi H. Degue, Jerome Le Ny

Large-scale monitoring and control systems enabling a more intelligent infrastructure increasingly rely on sensitive data obtained from private agents, e.g., location traces collected from the users of an intelligent transportation system. In order to encourage the participation of these agents, it becomes then critical to design algorithms that process information in a privacy-preserving way. This article revisits the Kalman filtering and Linear Quadratic Gaussian (LQG) control problems, subject to privacy constraints. We aim to enforce differential privacy, a formal, state-of-the-art definition of privacy ensuring that the output of an algorithm is not too sensitive to the data collected from any single participating agent. A two-stage architecture is proposed that first aggregates and combines the individual agent signals before adding privacy-preserving noise and post-filtering the result to be published. We show a significant performance improvement offered by this architecture over input perturbation schemes as the number of input signals increases and that an optimal static aggregation stage can be computed by solving a semidefinite program. The two-stage architecture, which we develop first for Kalman filtering, is then adapted to the LQG control problem by leveraging the separation principle. Numerical simulations illustrate the performance improvements over differentially private algorithms without first-stage signal aggregation.

Rabih Salhab, Roland P. Malhamé, Jerome Le Ny

We consider a class of dynamic collective choice models with social interactions, whereby a large number of non-uniform agents have to individually settle on one of multiple discrete alternative choices, with the relevance of their would-be choices continuously impacted by noise and the unfolding group behavior. This class of problems is modeled here as a so-called Min-LQG game, i.e., a linear quadratic Gaussian dynamic and non-cooperative game, with an additional combinatorial aspect in that it includes a final choice-related minimization in its terminal cost. The presence of this minimization term is key to enforcing some specific discrete choice by each individual agent. The theory of mean field games is invoked to generate a class of decentralized agent feedback control strategies which are then shown to converge to an exact Nash equilibrium of the game as the number of players increases to infinity. A key building block in our approach is an explicit solution to the problem of computing the best response of a generic agent to some arbitrarily posited smooth mean field trajectory. Ultimately, an agent is shown to face a continuously revised discrete choice problem, where greedy choices dictated by current conditions must be constantly balanced against the risk of the future process noise upsetting the wisdom of such decisions.Even though an agent's ultimately chosen alternative is random and dictated by its entire noise history and initial state, the limiting infinite population macroscopic behavior can still be predicted. It is shown that any Nash equilibrium of the game is defined by an a priori computable probability matrix characterizing the manner in which the agent population ultimately splits among the available alternatives.

Alexandre Borowczyk, Duc-Tien Nguyen, André Phu-Van Nguyen et al.

While autonomous multirotor micro aerial vehicles (MAVs) are uniquely well suited for certain types of missions benefiting from stationary flight capabilities, their more widespread usage still faces many hurdles, due in particular to their limited range and the difficulty of fully automating their deployment and retrieval. In this paper we address these issues by solving the problem of the automated landing of a quadcopter on a ground vehicle moving at relatively high speed. We present our system architecture, including the structure of our Kalman filter for the estimation of the relative position and velocity between the quadcopter and the landing pad, as well as our controller design for the full rendezvous and landing maneuvers. The system is experimentally validated by successfully landing in multiple trials a commercial quadcopter on the roof of a car moving at speeds of up to 50 km/h.

Manikandasriram Srinivasan Ramanagopal, André Phu-Van Nguyen, Jerome Le Ny

This paper presents a strategy to guide a mobile ground robot equipped with a camera or depth sensor, in order to autonomously map the visible part of a bounded three-dimensional structure. We describe motion planning algorithms that determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer. The emphasis is on accurately reconstructing a 3D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction and inspection. The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box, and the paths generated are well adapted to situations where the vision sensor is used simultaneously for mapping and for localizing the robot, in the absence of additional absolute positioning system. We analyze the coverage properties of our policy, and compare its performance to the classic frontier based exploration algorithm. We illustrate its efficacy for different structure sizes, levels of localization accuracy and range of the depth sensor, and validate our design on a real-world experiment.

Jerome Le Ny

Many large-scale information systems such as intelligent transportation systems, smart grids or smart buildings collect data about the activities of their users to optimize their operations. To encourage participation and adoption of these systems, it is becoming increasingly important that the design process take privacy issues into consideration. In a typical scenario, signals originate from many sensors capturing events involving the users, and several statistics of interest need to be continuously published in real-time. This paper considers the problem of providing differential privacy guarantees for such multi-input multi-output systems processing event streams. We show how to construct and optimize various extensions of the zero-forcing equalization mechanism, which we previously proposed for single-input single-output systems. Some of these extensions can take a model of the input signals into account. We illustrate our privacy-preserving filter design methodology through the problem of privately monitoring and forecasting occupancy in a building equipped with multiple motion detection sensors.

Nikolay A. Atanasov, Jerome Le Ny, George J. Pappas

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow the stochastic gradient of the mutual information between their expected measurements and the location of the source in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal.

Nikolay Atanasov, Bharath Sankaran, Jerome Le Ny et al.

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of views, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that our approach outperforms the widely-used greedy view point selection and provides a significant improvement over static object detection.

Nikolay Atanasov, Jerome Le Ny, Kostas Daniilidis et al.

Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.

Jerome Le Ny

Rigorous privacy mechanisms that can cope with dynamic data are required to encourage a wider adoption of large-scale monitoring and decision systems relying on end-user information. A promising approach to develop these mechanisms is to specify quantitative privacy requirements at design time rather than as an afterthought, and to rely on signal processing techniques to achieve satisfying trade-offs between privacy and performance specifications. This paper discusses, from the signal processing point of view, an event stream analysis problem introduced in the database and cryptography literature. A discrete-valued input signal describes the occurrence of events contributed by end-users, and a system is supposed to provide some output signal based on this information, while preserving the privacy of the participants. The notion of privacy adopted here is that of event-level differential privacy, which provides strong privacy guarantees and has important operational advantages. Several mechanisms are described to provide differentially private output signals while minimizing the impact on performance. These mechanisms demonstrate the benefits of leveraging system theoretic techniques to provide privacy guarantees for dynamic systems.

Jerome Le Ny, George J. Pappas

This paper studies the H2 (Kalman) filtering problem in the situation where a signal estimate must be constructed based on inputs from individual participants, whose data must remain private. This problem arises in emerging applications such as smart grids or intelligent transportation systems, where users continuously send data to third-party aggregators performing global monitoring or control tasks, and require guarantees that this data cannot be used to infer additional personal information. To provide strong formal privacy guarantees against adversaries with arbitrary side information, we rely on the notion of differential privacy introduced relatively recently in the database literature. This notion is extended to dynamic systems with many participants contributing independent input signals, and mechanisms are then proposed to solve the H2 filtering problem with a differential privacy constraint. A method for mitigating the impact of the privacy-inducing mechanism on the estimation performance is described, which relies on controlling the Hinfinity norm of the filter. Finally, we discuss an application to a privacy-preserving traffic monitoring system.

Jerome Le Ny, George J. Pappas

Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an undesirable loss of privacy for the users in exchange of the benefits provided by the application. Motivated by this trend, this paper introduces privacy concerns in a system theoretic context, and addresses the problem of releasing filtered signals that respect the privacy of the user data streams. Our approach relies on a formal notion of privacy from the database literature, called differential privacy, which provides strong privacy guarantees against adversaries with arbitrary side information. Methods are developed to approximate a given filter by a differentially private version, so that the distortion introduced by the privacy mechanism is minimized. Two specific scenarios are considered. First, the notion of differential privacy is extended to dynamic systems with many participants contributing independent input signals. Kalman filtering is also discussed in this context, when a released output signal must preserve differential privacy for the measured signals or state trajectories of the individual participants. Second, differentially private mechanisms are described to approximate stable filters when participants contribute to a single event stream, extending previous work on differential privacy under continual observation.