Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) is a powerful approach for robot learning.
Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 1904
Earlier work this paper cites.
A markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Learning to control a low-cost manipulator using data-efficient reinforcement learning
Marc Deisenroth, Carl Rasmussen, and Dieter Fox · 2011
Earlier work this paper cites.
Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 2015
Earlier work this paper cites.
From pixels to torques: Policy learning with deep dynamical models
Niklas Wahlström, Thomas B. Schön, and Marc P. Deisenroth · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
TP Lillicrap · 2015
Earlier work this paper cites.
Stable reinforcement learning with autoencoders for tactile and visual data
Herke Van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, and Jan Peters · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
Earlier work this paper cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Earlier work this paper cites.
Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
Earlier work this paper cites.
Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, and Nicolas Heess · 2018
Earlier work this paper cites.
Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2018
Earlier work this paper cites.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Earlier work this paper cites.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Earlier work this paper cites.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Earlier work this paper cites.
Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
Earlier work this paper cites.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
Earlier work this paper cites.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Earlier work this paper cites.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Earlier work this paper cites.
Learning to fly via deep model-based reinforcement learning
Philip Becker-Ehmck, Maximilian Karl, Jan Peters, and Patrick van der Smagt · 2020
Cited alongside, same era.
Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
Cited alongside, same era.
Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Cited alongside, same era.
Flightmare: A flexible quadrotor simulator
Yunlong Song, Selim Naji, Elia Kaufmann, Antonio Loquercio, and Davide Scaramuzza · 2020
Cited alongside, same era.
Improving sample efficiency in model-free reinforcement learning from images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2021
Cited alongside, same era.
Learning vision-based reactive policies for obstacle avoidance
Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
Later among the works it cites.
Transformer-based world models are happy with 100k interactions
Jan Robine, Marc Höftmann, Tobias Uelwer, and Stefan Harmeling · 2023
Later among the works it cites.
Storm: Efficient stochastic transformer based world models for reinforcement learning
Weipu Zhang, Gang Wang, Jian Sun, Yetian Yuan, and Gao Huang · 2023
Later among the works it cites.
Transformers are sample-efficient world models
Vincent Micheli, Eloi Alonso, and François Fleuret · 2023
Later among the works it cites.
Daydreamer: World models for physical robot learning
Philipp Wu, Alejandro Escontrela, Danijar Hafner, Pieter Abbeel, and Ken Goldberg · 2023
Later among the works it cites.
Simplified state space layers for sequence modeling
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer, and Sami Haddadin · 2021
Cited alongside, same era.
Differentiable physics models for real-world offline model-based reinforcement learning
Michael Lutter, Johannes Silberbauer, Joe Watson, and Jan Peters · 2021
Cited alongside, same era.
Robust policies via mid-level visual representations: An experimental study in manipulation and navigation
Bryan Chen, Alexander Sax, Francis Lewis, Iro Armeni, Silvio Savarese, Amir Zamir, Jitendra Malik, and Lerrel Pinto · 2021
Cited alongside, same era.
Learn2assemble with structured representations and search for robotic architectural construction
Niklas Funk, Georgia Chalvatzaki, Boris Belousov, and Jan Peters · 2022
Cited alongside, same era.
Learning to centralize dual-arm assembly
Marvin Alles and Elie Aljalbout · 2022
Cited alongside, same era.
Temporal difference learning for model predictive control
Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
Cited alongside, same era.
Transdreamer: Reinforcement learning with transformer world models
Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2022
Cited alongside, same era.
Jimmy T.H. Smith, Andrew Warrington, and Scott Linderman · 2023
Later among the works it cites.
Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al · 2024
Later among the works it cites.
Facing off world model backbones: Rnns, transformers, and s4
Fei Deng, Junyeong Park, and Sungjin Ahn · 2024
Later among the works it cites.
TD-MPC2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
Later among the works it cites.
Sample-efficient learning to solve a real-world labyrinth game using data-augmented model-based reinforcement learning
Thomas Bi and Raffaello D’Andrea · 2024
Later among the works it cites.
Twist: Teacher-student world model distillation for efficient sim-to-real transfer
Jun Yamada, Marc Rigter, Jack Collins, and Ingmar Posner · 2024
Later among the works it cites.
Contrastive learning for enhancing robust scene transfer in vision-based agile flight
Jiaxu Xing, Leonard Bauersfeld, Yunlong Song, Chunwei Xing, and Davide Scaramuzza · 2024
Later among the works it cites.
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
Sneha Silwal, Karmesh Yadav, Tingfan Wu, Jay Vakil, Arjun Majumdar, Sergio Arnaud, Claire Chen, Vincent-Pierre Berges, Dhruv Batra, Aravind Rajeswaran, et al · 2024
Later among the works it cites.
Mobile ALOHA: Learning bimanual mobile manipulation using low-cost whole-body teleoperation
Zipeng Fu, Tony Z. Zhao, and Chelsea Finn · 2024
Later among the works it cites.
Flare: Achieving masterful and adaptive robot policies with large-scale reinforcement learning fine-tuning
Jiaheng Hu, Rose Hendrix, Ali Farhadi, Aniruddha Kembhavi, Roberto Martín-Martín, Peter Stone, Kuo-Hao Zeng, and Kiana Ehsani · 2024
Later among the works it cites.
Badri Narayana Patro and Vijay Srinivas Agneeswaran · 2024
Later among the works it cites.
Structured state space models for in-context reinforcement learning
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, and Feryal Behbahani · 2024
Later among the works it cites.
Demonstrating agile flight from pixels without state estimation
Ismail Geles, Leonard Bauersfeld, Angel Romero, Jiaxu Xing, and Davide Scaramuzza · 2024
Later among the works it cites.
Actor-critic model predictive control: Differentiable optimization meets reinforcement learning
Angel Romero, Elie Aljalbout, Yunlong Song, and Davide Scaramuzza · 2024
Later among the works it cites.
Dream to fly: Model-based reinforcement learning for vision-based drone flight
Angel Romero, Ashwin Shenai, Ismail Geles, Elie Aljalbout, and Davide Scaramuzza · 2025
Closest in time.
Student-informed teacher training
Nico Messikommer, Jiaxu Xing, Elie Aljalbout, and Davide Scaramuzza · 2025
Closest in time.