Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes

2 June 2023

Papers citing "Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes"

12 / 12 papers shown

Title
Partially Observable Markov Decision Processes in Robotics: A Survey M. Lauri David Hsu J. Pajarinen 51 92 0 21 Sep 2022
Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned Marco Tranzatto M. Dharmadhikari Lukas Bernreiter Marco Camurri Shehryar Khattak ... M. Mueller Maurice F. Fallon Roland Siegwart Marco Hutter K. Alexis 29 34 0 11 Jul 2022
AK: Attentive Kernel for Information Gathering Weizhe (Wesley) Chen R. Khardon Lantao Liu 32 12 0 13 May 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 45 55 0 23 Feb 2022
Informative Path Planning to Estimate Quantiles for Environmental Analysis Isabel M. Rayas Fernández Chris Denniston D. Caron Gaurav Sukhatme 18 16 0 25 Jan 2022
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing Julius Ruckin Liren Jin Marija Popović 58 46 0 28 Sep 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 46 21 0 20 Sep 2021
Data-efficient Domain Randomization with Bayesian Optimization Fabio Muratore C. Eilers Michael Gienger Jan Peters OOD 68 77 0 05 Mar 2020
Adversarial Generation of Informative Trajectories for Dynamics System Identification Marija Jegorova Joshua R. Smith M. Mistry Timothy M. Hospedales 19 7 0 02 Mar 2020
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm Giovanni Sutanto Austin S. Wang Yixin Lin Mustafa Mukadam Gaurav Sukhatme Akshara Rai Franziska Meier 86 54 0 24 Jan 2020
Informative Planning and Online Learning with Sparse Gaussian Processes Kai-Chieh Ma Lantao Liu Gaurav Sukhatme 42 75 0 24 Sep 2016
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 84 271 0 24 Feb 2014