Estimating and reacting to disturbances is crucial for robust flight control of quadrotors.
Existing estimators typically require significant tuning for a specific flight scenario or training …

This paper proposes a Distributed Moving Horizon Estimation (DMHE) approach performed
by an external static Sensor Network (SN) composed of surveillance cameras and their …

In this paper, we study state estimation for general nonlinear systems with unknown
parameters and persistent process and measurement noise. In particular, we are interested …

This paper presents a reinforcement learning-based observer/controller using Moving
Horizon Estimation (MHE) and Model Predictive Control (MPC) schemes where the models …

We study the problem of learning a function that maps context observations (input) to
parameters of a submodular function (output). Our motivating case study is a specific type of …

Accurate disturbance estimation is essential for safe robot operations. The recently
proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural …

This paper introduces a novel optimization-based approach for parametric nonlinear system
identification. Building upon the prediction error method framework, traditionally used for …

In this paper, we present a differentiable, decision-oriented learning technique for a class of
vehicle routing problems. Specifically, we consider a scenario where a team of Unmanned …

In this work, an innovative data-driven moving horizon state estimation is proposed for
model dynamic-unknown systems based on Bayesian optimization. As long as the …

State estimation is an important aspect in many robotics applications. In this work, we
consider the task of obtaining accurate state estimates for robotic systems by enhancing the …