Gaussian Processes for Data-Efficient Learning in Robotics and Control

10 February 2015

Papers citing "Gaussian Processes for Data-Efficient Learning in Robotics and Control"

50 / 235 papers shown

Title
Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees Ewerton R. Vieira Aravind Sivaramakrishnan Yao Song Edgar Granados Marcio Gameiro Konstantin Mischaikow Ying Hung Kostas E. Bekris AI4CE 35 3 0 04 Oct 2022
Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning Péter Antal Tamás Péni R. Tóth 45 8 0 29 Sep 2022
Focused Adaptation of Dynamics Models for Deformable Object Manipulation Peter Mitrano A. LaGrassa Oliver Kroemer Dmitry Berenson AI4CE 37 11 0 28 Sep 2022
FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization Hardik Parwana Dimitra Panagou 28 2 0 26 Sep 2022
Non-Gaussian Process Regression Y. Kindap S. Godsill GP 27 1 0 07 Sep 2022
Elly: A Real-Time Failure Recovery and Data Collection System for Robotic Manipulation Elena Galbally Adrian Piedra Cynthia Brosque O. Khatib 23 0 0 25 Aug 2022
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics Zhichao Li T. Duong Nikolay Atanasov 19 1 0 22 Jul 2022
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes G. Evangelisti Sandra Hirche 14 15 0 24 Jun 2022
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control Katie Kang Paula Gradu Jason J. Choi Michael Janner Claire Tomlin Sergey Levine 19 23 0 21 Jun 2022
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Souradip Chakraborty Amrit Singh Bedi Alec Koppel Brian M. Sadler Furong Huang Pratap Tokekar Tianyi Zhou 31 9 0 02 Jun 2022
A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems Cheng Feng OffRL AI4CE 38 0 0 15 May 2022
Gaussian Process Self-triggered Policy Search in Weakly Observable Environments Hikaru Sasaki T. Hirabayashi Kaoru Kawabata Takamitsu Matsubara 14 2 0 07 May 2022
Gradient-Based Trajectory Optimization With Learned Dynamics Bhavya Sukhija Nathanael Kohler Miguel Zamora Simon Zimmermann Sebastian Curi Andreas Krause Stelian Coros 32 9 0 09 Apr 2022
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations Hao Chen Liang Ding Rui Tuo 12 10 0 07 Mar 2022
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control Abdolreza Taheri Joni Pajarinen R. Ghabcheloo GP 19 3 0 28 Feb 2022
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings Adam J. Thorpe T. Lew Meeko Oishi Marco Pavone 35 21 0 08 Feb 2022
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes Hamed Jalali Gjergji Kasneci FedML 27 0 0 07 Feb 2022
Physics-guided Learning-based Adaptive Control on the SE(3) Manifold T. Duong Nikolay Atanasov PINN DRL AI4CE 46 0 0 12 Jan 2022
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators Jan Brüdigam Martin Schuck A. Capone Stefan Sosnowski Sandra Hirche 19 4 0 10 Dec 2021
Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics Zhichao Li T. Duong Nikolay Atanasov 32 2 0 09 Dec 2021
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes Qin Lu G. Giannakis GP OffRL 27 4 0 01 Dec 2021
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling Xubo Yue Raed Al Kontar FedML 58 9 0 28 Nov 2021
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks Giacomo Arcieri David Wölfle Eleni Chatzi OffRL 27 5 0 25 Oct 2021
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning Akhil Nagariya D. Kalathil Srikanth Saripalli OffRL 25 1 0 05 Oct 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications A. Capone Armin Lederer Sandra Hirche 32 18 0 06 Sep 2021
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning Remo Sasso M. Sabatelli M. Wiering CLL OffRL 35 6 0 14 Aug 2021
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making Debabrata Mahapatra Vaibhav Rajan 33 2 0 02 Aug 2021
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference Michael E. Kepler Alec Koppel Amrit Singh Bedi D. Stilwell 17 3 0 26 Jul 2021
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? Balázs Kégl Gabriel Hurtado Albert Thomas 17 12 0 24 Jul 2021
3D Radar Velocity Maps for Uncertain Dynamic Environments Ransalu Senanayake Kyle Hatch J. Zheng Mykel J. Kochenderfer 16 1 0 23 Jul 2021
Active Learning in Robotics: A Review of Control Principles Annalisa T. Taylor Thomas A. Berrueta Todd D. Murphey 32 71 0 25 Jun 2021
Learning Nonparametric Volterra Kernels with Gaussian Processes M. Ross M. Smith Mauricio A. Alvarez GP 24 7 0 10 Jun 2021
Gaussian Processes on Hypergraphs Thomas Pinder K. Turnbull Christopher Nemeth David Leslie 33 4 0 03 Jun 2021
Partitioned Active Learning for Heterogeneous Systems Cheolhei Lee Kaiwen Wang Jianguo Wu W. Cai Xiaowei Yue 11 14 0 14 May 2021
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning Luis Pineda Brandon Amos Amy Zhang Nathan Lambert Roberto Calandra OffRL 33 46 0 20 Apr 2021
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting Eric Benhamou David Saltiel S. Tabachnik Sui Kai Wong François Chareyron OOD 57 4 0 19 Apr 2021
Data-Driven Robust Barrier Functions for Safe, Long-Term Operation Y. Emam Paul Glotfelter S. Wilson Gennaro Notomista M. Egerstedt 19 25 0 15 Apr 2021
Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds Didong Li Wenpin Tang Sudipto Banerjee 28 14 0 08 Apr 2021
Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes M. Villani M. Quiroz Robert Kohn R. Salomone AI4TS 11 7 0 05 Apr 2021
Discriminator Augmented Model-Based Reinforcement Learning Behzad Haghgoo Allan Zhou Archit Sharma Chelsea Finn OffRL 19 3 0 24 Mar 2021
Data-driven Aerodynamic Analysis of Structures using Gaussian Processes I. Kavrakov A. McRobie Guido Morgenthal 8 13 0 20 Mar 2021
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control I. D. Rodriguez Ugo Rosolia Aaron D. Ames Yisong Yue 38 2 0 08 Mar 2021
Learning Contact Dynamics using Physically Structured Neural Networks Andreas Hochlehnert Alexander Terenin Steindór Sæmundsson M. Deisenroth 19 16 0 22 Feb 2021
On the Universal Transformation of Data-Driven Models to Control Systems Sebastian Peitz Katharina Bieker AI4CE 17 11 0 09 Feb 2021
Functional optimal transport: map estimation and domain adaptation for functional data Jiacheng Zhu Aritra Guha Dat Do Mengdi Xu X. Nguyen Ding Zhao OT 37 7 0 07 Feb 2021
Gaussian Experts Selection using Graphical Models Hamed Jalali Martin Pawelczyk Gjerji Kasneci 19 3 0 02 Feb 2021
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application Fabio Amadio Alberto Dalla Libera R. Antonello D. Nikovski R. Carli Diego Romeres 14 27 0 28 Jan 2021
Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems Junya Ikemoto T. Ushio OffRL 27 5 0 13 Jan 2021
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments Baiming Chen Zuxin Liu Jiacheng Zhu Mengdi Xu Wenhao Ding Ding Zhao 25 35 0 02 Jan 2021
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL Simon Hirlaender N. Bruchon 11 23 0 17 Dec 2020