This paper proves convergence of belief propagation algorithms for multipath data association to a unique fixed point.

This paper evaluates two approaches for maintaining safe separation between small Unmanned Aircraft Systems (sUAS) in urban environments with degraded Global Navigation Satellite System (GNSS) signals…

This paper theoretically justifies the strong performance of linear recurrent neural networks as memory units in partially observable reinforcement learning by constructing specific linear filters tha…

Proposed an asynchronous block coordinate descent algorithm for distributed trajectory estimation in robotics, reducing communications by up to 96.9% and achieving exponential convergence.

This paper introduces mean field reinforcement learning through Markov decision processes in large-population stochastic control, developing the necessary framework for representative-agent learning,…

The paper proposes the Frequency-Weighted Neural Kalman Filter (FW-NKF), a hybrid approach that improves state estimation for robotics by explicitly suppressing frequency-dependent noise components in…

A lightweight sensor-driven Lévy walk controller is presented for efficient autonomous exploration of minimal-sensing, resource-constrained nano-UAVs.

This paper develops scalable methods for time-series analysis using tensor algebra in factorial hidden Markov models, improving computational performance and enabling analysis of large systems.

This paper proposes a Fisher information density (FID)-constrained trajectory sharing scheme for robot collision avoidance under GDPR regulations, achieving better privacy-utility tradeoff than fixed-…

The paper introduces MINTS, a minimalist Bayesian framework that simplifies sequential decision-making by placing priors only on the optimum location, allowing for the incorporation of structural cons…

This paper compares the performance of open-loop and closed-loop filters in inertial navigation systems using simulations.

This paper analyzes the Bayesian fixed-budget best-arm identification problem with abstention, showing that it induces a phase transition from polynomial to exponential decay of error probability.

This paper investigates the necessity of interaction for order-optimal 1-bit mean estimation in nonparametric finite-moment classes.

This paper trains sparse sensor policies for Rayleigh-Bénard convection control using multi-agent reinforce learning and grouped regularization.

This paper introduces a stochastic differential equation approximation for linear Temporal Difference (TD) learning under Markovian noise, explaining the constant-stepsize error floor.

The paper proposes a novel Bayesian framework to learn the optimal decision strategy for the stochastic shortest path problem by directly constructing the posterior beliefs for the action-value functi…

This paper proposes the RFFBCGA algorithm, a random Fourier feature based bias-compensated filter that mitigates input noise interference and enhances robustness in nonlinear adaptive filtering.

This paper provides non-asytotic sample complexity guarantees for the Navigate and Stop algorithm in online tabular Reinforcement Learning, identifying additional attributes that affect the overall sa…

The paper proposes an algorithmic method using conformal prediction to formally certify high-probability safety for Belief-Space Neural Safety Filters (BeliefSF), significantly improving safety guaran…