Reduced Order Observer for Structure from Motion using Concurrent Learning

Rotithor, Ghananeel; Trombetta, Daniel; Kamalapurkar, Rushikesh; Dani, Ashwin P.

doi:10.1109/cdc40024.2019.9029636

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…The second observer implements a novel idea of re-using previous system state estimates and control measurements, along with the Hamilton-Jacobi-Bellman equation, to gain insights into the quality of the current estimate of the reward function. The key advantage of the IRL history stack observer (HSO) over MLO is that it provides an additional guarantee for boundedness of the estimation errors under finite (as opposed to persistent) excitation [24].…”

Section: Introductionmentioning

confidence: 99%

Online Observer-Based Inverse Reinforcement Learning

Self,

Coleman,

Bai

et al. 2020

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In this paper, a novel approach to the outputfeedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.

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Section: Introductionmentioning

confidence: 99%

Online Observer-Based Inverse Reinforcement Learning

Self,

Coleman,

Bai

et al. 2020

Preprint

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“…For these cases the camera motion will not satisfy the PE condition for certain time window. Compared to the recent work in [27], [28], a rigorous stability analysis and detailed simulation and experimental evaluations are presented in this paper. A history stack update procedure based on the Lyapunov analysis, which stores the camera motion and feature point data, is presented.…”

Section: Introductionmentioning

confidence: 99%

Extension of Full and Reduced Order Observers for Image-based Depth Estimation using Concurrent Learning

Rotithor,

Trombetta,

Kamalapurkar

et al. 2020

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In this paper concurrent learning (CL)-based full and reduced order observers for a perspective dynamical system (PDS) are developed. The PDS is a widely used model for estimating the depth of a feature point from a sequence of camera images. Building on the current progress of CL for parameter estimation in adaptive control, a state observer is developed for the PDS model where the inverse depth appears as a time-varying parameter in the dynamics. The data recorded over a sliding time window in the near past is used in the CL term to design the full and the reduced order state observers. A Lyapunov-based stability analysis is carried out to prove the uniformly ultimately bounded (UUB) stability of the developed observers. Simulation results are presented to validate the accuracy and convergence of the developed observers in terms of convergence time, root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. Real world depth estimation experiments are performed to demonstrate the performance of the observers using aforementioned metrics on a 7-DoF manipulator with an eye-in-hand configuration.

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