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
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning Mohammad Babaeizadeh M. Saffar Danijar Hafner Harini Kannan Chelsea Finn Sergey Levine D. Erhan VLM 27 9 0 08 Dec 2020
Neural Dynamic Policies for End-to-End Sensorimotor Learning Shikhar Bahl Mustafa Mukadam Abhinav Gupta Deepak Pathak 24 83 0 04 Dec 2020
Multimodal dynamics modeling for off-road autonomous vehicles J. Tremblay Travis Manderson Aurélio Noca Gregory Dudek David Meger 26 16 0 23 Nov 2020
The Impact of Data on the Stability of Learning-Based Control- Extended Version Armin Lederer A. Capone Thomas Beckers Jonas Umlauft Sandra Hirche 9 10 0 20 Nov 2020
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning Jung-Su Ha Young-Jin Park Hyeok-Joo Chae Soon-Seo Park Han-Lim Choi 35 3 0 16 Nov 2020
Learning ODE Models with Qualitative Structure Using Gaussian Processes Steffen Ridderbusch Christian Offen Sina Ober-Blobaum Paul Goulart 16 15 0 10 Nov 2020
Model-based Reinforcement Learning from Signal Temporal Logic Specifications Parv Kapoor Anand Balakrishnan Jyotirmoy V. Deshmukh 29 22 0 10 Nov 2020
Planning under Uncertainty to Goal Distributions Adam Conkey Tucker Hermans 21 3 0 09 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 58 0 08 Nov 2020
Gaussian Processes Model-based Control of Underactuated Balance Robots Kuo Chen J. Yi Dezhen Song 18 19 0 29 Oct 2020
Forethought and Hindsight in Credit Assignment Veronica Chelu Doina Precup H. V. Hasselt 22 25 0 26 Oct 2020
Aggregating Dependent Gaussian Experts in Local Approximation Hamed Jalali Gjergji Kasneci 21 4 0 17 Oct 2020
Variable impedance control and learning -- A review Fares J. Abu-Dakka Matteo Saveriano AI4CE 41 148 0 13 Oct 2020
Improving Sequential Latent Variable Models with Autoregressive Flows Joseph Marino Lei Chen Jiawei He Stephan Mandt BDL AI4TS 30 12 0 07 Oct 2020
Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models Lei M. Zhang Matthias Plappert Wojciech Zaremba 19 4 0 27 Sep 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 22 7 0 25 Sep 2020
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems Vinicius G. Goecks 33 11 0 30 Aug 2020
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI Katya Kudashkina P. Pilarski R. Sutton KELM 25 6 0 27 Aug 2020
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework T. Lew Apoorva Sharma James Harrison Andrew Bylard Marco Pavone 28 44 0 26 Aug 2020
Fast Approximate Multi-output Gaussian Processes V. Joukov Dana Kulic 11 5 0 22 Aug 2020
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations Elena Arcari Andrea Carron Melanie Zeilinger 23 14 0 13 Aug 2020
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey Aske Plaat W. Kosters Mike Preuss BDL OffRL 21 17 0 11 Aug 2020
Chance Constrained Policy Optimization for Process Control and Optimization Panagiotis Petsagkourakis I. O. Sandoval E. Bradford F. Galvanin Dongda Zhang Ehecatl Antonio del Rio Chanona 19 36 0 30 Jul 2020
An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model Akhil Nagariya Srikanth Saripalli 27 28 0 28 Jul 2020
Anticipating the Long-Term Effect of Online Learning in Control A. Capone Sandra Hirche 19 4 0 24 Jul 2020
Matérn Gaussian processes on Riemannian manifolds Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 22 120 0 17 Jun 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi Felix Berkenkamp Andreas Krause 38 82 0 15 Jun 2020
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes Yash Nair Finale Doshi-Velez OffRL 52 2 0 11 Jun 2020
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty Panagiotis Petsagkourakis I. O. Sandoval E. Bradford Dongda Zhang Ehecatl Antonio del Rio Chanona 6 11 0 04 Jun 2020
Localized active learning of Gaussian process state space models A. Capone Jonas Umlauft Thomas Beckers Armin Lederer Sandra Hirche 13 27 0 04 May 2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming Gabriel Riutort-Mayol Paul-Christian Bürkner Michael R. Andersen Arno Solin Aki Vehtari 32 68 0 23 Apr 2020
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck Alec Koppel Hrusikesha Pradhan K. Rajawat 8 32 0 23 Apr 2020
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels I. Goumiri Benjamin W. Priest M. Schneider GP BDL 14 6 0 10 Apr 2020
Planning and Execution using Inaccurate Models with Provable Guarantees Anirudh Vemula Yash Oza J. Andrew Bagnell Maxim Likhachev 16 18 0 09 Mar 2020
Policy-Aware Model Learning for Policy Gradient Methods Romina Abachi Mohammad Ghavamzadeh Amir-massoud Farahmand 22 34 0 28 Feb 2020
Autonomous robotic nanofabrication with reinforcement learning Philipp Leinen Malte Esders Kristof T. Schütt C. Wagner K. Müller F. Tautz 22 52 0 27 Feb 2020
Efficiently Sampling Functions from Gaussian Process Posteriors James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 14 163 0 21 Feb 2020
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process Wei Cao Xiaowei Yue Ben Haaland C. F. Jeff Wu 14 14 0 04 Feb 2020
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness George Wynne F. Briol Mark Girolami 24 55 0 29 Jan 2020
Tuneful: An Online Significance-Aware Configuration Tuner for Big Data Analytics Ayat Fekry Lucian Carata Thomas Pasquier Andrew Rice A. Hopper 34 16 0 22 Jan 2020
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing Elena Arcari Lukas P. Frohlich Melanie Zeilinger 25 35 0 23 Dec 2019
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos Laura M. Smith Nikita Dhawan Marvin Zhang Pieter Abbeel Sergey Levine 43 156 0 10 Dec 2019
Scaling active inference Alexander Tschantz Manuel Baltieri A. Seth Christopher L. Buckley BDL AI4CE 19 68 0 24 Nov 2019
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation Pietro Falco Abdallah Attawia Matteo Saveriano Dongheui Lee 19 29 0 20 Nov 2019
Safe Interactive Model-Based Learning Marco Gallieri Seyed Sina Mirrazavi Salehian N. E. Toklu A. Quaglino Jonathan Masci Jan Koutník Faustino J. Gomez 14 12 0 15 Nov 2019
RoboNet: Large-Scale Multi-Robot Learning Sudeep Dasari F. Ebert Stephen Tian Suraj Nair Bernadette Bucher Karl Schmeckpeper Siddharth Singh Sergey Levine Chelsea Finn LM&Ro 34 296 0 24 Oct 2019
Multi-View Reinforcement Learning Minne Li Lisheng Wu Haitham Bou-Ammar Jun Wang 21 26 0 18 Oct 2019
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning S. Rezaei-Shoshtari David Meger I. Sharf 6 12 0 05 Oct 2019
Model Imitation for Model-Based Reinforcement Learning Yueh-hua Wu Ting-Han Fan Peter J. Ramadge H. Su OffRL 21 16 0 25 Sep 2019
PAC-Bayesian Bounds for Deep Gaussian Processes R. Foll Ingo Steinwart BDL 33 1 0 22 Sep 2019