no code implementations • 16 Mar 2024 • Tian Zhao, Timothy L. Molloy
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.
no code implementations • 21 Feb 2023 • Yitian Chen, Timothy L. Molloy, Tyler Summers, Iman Shames
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.
no code implementations • 3 Nov 2022 • Timothy L. Molloy, Iman Shames
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.
no code implementations • 22 Dec 2021 • Timothy L. Molloy, Girish N. Nair
We investigate partially observed Markov decision processes (POMDPs) with cost functions regularized by entropy terms describing state, observation, and control uncertainty.
no code implementations • 19 Aug 2021 • Timothy L. Molloy, Girish N. Nair
We study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory.
no code implementations • 4 Apr 2021 • Timothy L. Molloy, Girish N. Nair
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.
no code implementations • 23 Mar 2021 • Timothy L. Molloy, Girish N. Nair
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.
no code implementations • 19 Oct 2020 • Timothy L. Molloy, Tobias Fischer, Michael Milford, Girish N. Nair
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.
no code implementations • 31 Aug 2020 • Jason J. Ford, Jasmin James, Timothy L. Molloy
This paper considers the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting.