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
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning Jan Ole von Hartz Adrian Rofer Joschka Boedecker Abhinav Valada 41 0 0 06 May 2025
GPRat: Gaussian Process Regression with Asynchronous Tasks Maksim Helmann Alexander Strack Dirk Pflüger GP 43 0 0 30 Apr 2025
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 59 0 0 24 Mar 2025
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games Shubhankar Agarwal Hamzah I. Khan Sandeep Chinchali David Fridovich-Keil 46 0 0 23 Mar 2025
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation Eliot Xing Vernon Luk Jean Oh 99 0 0 16 Dec 2024
Continuous-Time State Estimation Methods in Robotics: A Survey William Talbot Julian Nubert Turcan Tuna Cesar Cadena Frederike Dumbgen J. Tordesillas T. Barfoot Marco Hutter 74 8 0 06 Nov 2024
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling Jasmine Bayrooti Carl Henrik Ek Amanda Prorok 50 0 0 07 Oct 2024
Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions Yuji Ito 23 0 0 19 Sep 2024
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models Avirup Das Rishabh Dev Yadav Sihao Sun Mingfei Sun Samuel Kaski Wei Pan 26 2 0 17 Sep 2024
Formal Verification and Control with Conformal Prediction Lars Lindemann Yiqi Zhao Xinyi Yu George J. Pappas Jyotirmoy Deshmukh 78 15 0 31 Aug 2024
Aggregation Models with Optimal Weights for Distributed Gaussian Processes Liam Hebert Sukhdeep S. Sodhi 29 0 0 01 Aug 2024
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation Detian Chu Linyuan Bai Jianuo Huang Zhenlong Fang Peng Zhang Wei Kang Haifeng Lin 50 2 0 08 Jul 2024
KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation Hongyi Chen Abulikemu Abuduweili Aviral Agrawal Yunhai Han Harish Ravichandar Changliu Liu Jeffrey Ichnowski 57 5 0 29 Jun 2024
Probabilistic Subgoal Representations for Hierarchical Reinforcement learning V. Wang Tinghuai Wang Wenyan Yang Joni-Kristian Kämäräinen Joni Pajarinen BDL 28 3 0 24 Jun 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 46 2 0 28 May 2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato 41 1 0 28 May 2024
Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robots Christopher Coco Jonathan Spanos Hamid Osooli Reza Azadeh 13 1 0 03 May 2024
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search Can Bogoclu Robert Vosshall K. Cremanns Dirk Roos BDL 25 1 0 23 Mar 2024
$Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control$ Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control Minjun Sung Sambhu H. Karumanchi Aditya Gahlawat N. Hovakimyan 36 1 0 21 Mar 2024
Decomposing Control Lyapunov Functions for Efficient Reinforcement Learning Antonio Lopez David Fridovich-Keil 40 1 0 18 Mar 2024
Generalizing Cooperative Eco-driving via Multi-residual Task Learning Vindula Jayawardana Sirui Li Cathy Wu Y. Farid Kentaro Oguchi 30 3 0 07 Mar 2024
Incremental Bayesian Learning for Fail-Operational Control in Autonomous Driving Lei Zheng Rui Yang Zeng Peng Wei Yan Michael Yu Wang Jun Ma 47 2 0 07 Mar 2024
Gradient-enhanced deep Gaussian processes for multifidelity modelling Viv Bone Chris van der Heide Kieran Mackle Ingo Jahn P. Dower Chris Manzie 27 1 0 25 Feb 2024
Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration Yigit Yildirim Emre Ugur 21 3 0 13 Feb 2024
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty T. Faulwasser O. Molodchyk 43 1 0 12 Dec 2023
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Zhidi Lin Yiyong Sun Feng Yin Alexandre Thiéry 37 4 0 10 Dec 2023
Sparse Variational Student-t Processes Jian Xu Delu Zeng 39 1 0 09 Dec 2023
Spatial Process Approximations: Assessing Their Necessity Hao Zhang 21 2 0 06 Nov 2023
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression Harris Papadopoulos 37 11 0 24 Oct 2023
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach Yu Wang Yuxuan Yin Karthik Somayaji Nanjangud Suryanarayana Ján Drgoňa Malachi Schram M. Halappanavar Frank Liu Peng Li 19 0 0 19 Oct 2023
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning Parvin Malekzadeh Ming Hou Konstantinos N. Plataniotis 53 1 0 16 Oct 2023
Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives Muhammad Kazim JunGee Hong Min-Gyeom Kim Kwang-Ki K. Kim 44 16 0 22 Sep 2023
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling Wenjun Huang Yunduan Cui Huiyun Li Xin Wu MU 27 0 0 20 Sep 2023
Memory-based Controllers for Efficient Data-driven Control of Soft Robots Yuzhe Wu Ehsan Nekouei 9 3 0 19 Sep 2023
Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning Marin Vlastelica Sebastian Blaes Cristina Pinneri Georg Martius 11 1 0 11 Sep 2023
On minimizing the training set fill distance in machine learning regression Paolo Climaco Jochen Garcke 15 1 0 20 Jul 2023
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors Armin Lederer Jonas Umlauft Sandra Hirche 52 0 0 10 Jul 2023
Risk-Averse Trajectory Optimization via Sample Average Approximation T. Lew Riccardo Bonalli Marco Pavone 31 9 0 06 Jul 2023
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling Elizaveta Semenova Prakhar Verma Max Cairney-Leeming Arno Solin Samir Bhatt Seth Flaxman BDL 30 3 0 09 Apr 2023
GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics Francesco Crocetti Jeffrey Mao Alessandro Saviolo G. Costante Giuseppe Loianno GP 22 5 0 14 Mar 2023
A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems Navid Hashemi Bardh Hoxha Tomoya Yamaguchi Danil Prokhorov Geogios Fainekos Jyotirmoy Deshmukh 35 8 0 07 Mar 2023
Sharp Calibrated Gaussian Processes A. Capone Geoff Pleiss Sandra Hirche UQCV 47 4 0 23 Feb 2023
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning Palash Chatterjee Ashutosh Chapagain Weizhe (Wesley) Chen Roni Khardon 16 1 0 03 Feb 2023
Learning Control from Raw Position Measurements Fabio Amadio Alberto Dalla Libera D. Nikovski R. Carli Diego Romeres 20 8 0 30 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 36 7 0 21 Jan 2023
Scalable Gaussian Process Inference with Stan Till Hoffmann J. Onnela GP 24 4 0 21 Jan 2023
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels Antonio Candelieri Andrea Ponti Francesco Archetti 24 1 0 02 Dec 2022
Gaussian Process Barrier States for Safe Trajectory Optimization and Control Hassan Almubarak Manan S. Gandhi Yuichiro Aoyama N. Sadegh Evangelos A. Theodorou 36 0 0 01 Dec 2022
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies David van de Sanden Maarten Schoukens Mauro Salazar 12 2 0 27 Nov 2022
Safe Policy Improvement in Constrained Markov Decision Processes Luigi Berducci Radu Grosu OffRL 38 2 0 20 Oct 2022