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Found 9 papers, 0 papers with code
Extended Kalman Filtering for Recursive Online Discrete-Time Inverse Optimal Control
We formulate the discrete-time inverse optimal control problem of inferring unknown parameters in the objective function of an optimal control problem from measurements of optimal states and controls as a nonlinear filtering problem.
Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking: Extended Version
We adopted the notion of dynamic regret to measure the performance of this proposed online LQR control method, with our main result being that the (dynamic) regret of our method is upper bounded by a constant.
Minimum-Time Escape from a Circular Region for a Dubins Car
We investigate the problem of finding paths that enable a robot modeled as a Dubins car (i. e., a constant-speed finite-turn-rate unicycle) to escape from a circular region of space in minimum time.
Entropy-Regularized Partially Observed Markov Decision Processes
We investigate partially observed Markov decision processes (POMDPs) with cost functions regularized by entropy terms describing state, observation, and control uncertainty.
Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs
We study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory.
Active Trajectory Estimation for Partially Observed Markov Decision Processes via Conditional Entropy
By establishing a novel form of the smoother entropy in terms of the POMDP belief (or information) state, we show that our active smoothing problem can be reformulated as a (fully observed) Markov decision process with a value function that is concave in the belief state.
Smoothing-Averse Control: Covertness and Privacy from Smoothers
In this paper we investigate the problem of controlling a partially observed stochastic dynamical system such that its state is difficult to infer using a (fixed-interval) Bayesian smoother.
Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion
A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions.
Exactly Optimal Bayesian Quickest Change Detection for Hidden Markov Models
This paper considers the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting.
