Fetching the paper…
Reading the bibliography…
We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision.
Reinforcement Learning with Multi-Fidelity Simulators
M. Cutler, T. J. Walsh, and J. P. How · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Learning Compound Multi-Step Controllers under Unknown Dynamics
W. Han, S. Levine, and P. Abbeel · 2015
Earlier work this paper cites.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
Earlier work this paper cites.
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2017
Earlier work this paper cites.
Visual Closed-Loop Control for Pouring Liquids
C. Schenck and D. Fox · 2017
Earlier work this paper cites.
The DARPA Robotics Challenge Finals: Results and Perspectives
E. Krotkov, D. Hackett, L. Jackel, M. Perschbacher, J. Pippine, J. Strauss, G. Pratt, and C. Orlowski · 2017
Earlier work this paper cites.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
Earlier work this paper cites.
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition
J. Fu, A. Singh, D. Ghosh, L. Yang, and S. Levine · 2018
Earlier work this paper cites.
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
A. Gupta, A. Murali, D. P. Gandhi, and L. Pinto · 2018
Earlier work this paper cites.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
Earlier work this paper cites.
Solving Rubik’s Cube with a Robot Hand
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, et al · 2019
Earlier work this paper cites.
End-to-End Robotic Reinforcement Learning without Reward Engineering
A. Singh, L. Yang, K. Hartikainen, C. Finn, and S. Levine · 2019
Earlier work this paper cites.
Avid: Learning multi-stage tasks via pixel-level translation of human videos
L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine · 2019
Earlier work this paper cites.
Deep Dynamics Models for Learning Dexterous Manipulation
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar · 2020
Earlier work this paper cites.
Learning to Walk in the Real World with Minimal Human Effort
S. Ha, P. Xu, Z. Tan, S. Levine, and J. Tan · 2020
Earlier work this paper cites.
The Ingredients of Real-World Robotic Reinforcement Learning
H. Zhu, J. Yu, A. Gupta, D. Shah, K. Hartikainen, A. Singh, V. Kumar, and S. Levine · 2020
Earlier work this paper cites.
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, and S. Levine · 2020
Earlier work this paper cites.
A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
M. Bajracharya, J. Borders, D. Helmick, T. Kollar, M. Laskey, J. Leichty, J. Ma, U. Nagarajan, A. Ochiai, J. Petersen, et al · 2020
Cited alongside, same era.
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman · 2021
Cited alongside, same era.
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
A. Gupta, J. Yu, T. Z. Zhao, V. Kumar, A. Rovinsky, K. Xu, T. Devlin, and S. Levine · 2021
Cited alongside, same era.
ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation
F. Xia, C. Li, R. Martín-Martín, O. Litany, A. Toshev, and S. Savarese · 2021
Cited alongside, same era.
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
X. Chen, C. Wang, Z. Zhou, and K. Ross · 2021
DayDreamer: World Models for Physical Robot Learning
P. Wu, A. Escontrela, D. Hafner, K. Goldberg, and P. Abbeel · 2023
Later among the works it cites.
Deep rl at scale: Sorting waste in office buildings with a fleet of mobile manipulators
A. Herzog, K. Rao, K. Hausman, Y. Lu, P. Wohlhart, M. Yan, J. Lin, M. G. Arenas, T. Xiao, D. Kappler, et al · 2023
Later among the works it cites.
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
K. Xu, Z. Hu, R. Doshi, A. Rovinsky, V. Kumar, A. Gupta, and S. Levine · 2023
Later among the works it cites.
Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning
A. Gupta, C. Lynch, B. Kinman, G. Peake, S. Levine, and K. Hausman · 2023
Later among the works it cites.
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
A. Sharma, A. M. Ahmed, R. Ahmad, and C. Finn · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
D. Yarats, R. Fergus, and I. Kostrikov · 2021
Cited alongside, same era.
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World
L. Smith, J. C. Kew, X. B. Peng, S. Ha, J. Tan, and S. Levine · 2022
Cited alongside, same era.
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine · 2022
Cited alongside, same era.
Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment
H. Liu, S. Nasiriany, L. Zhang, Z. Bao, and Y. Zhu · 2022
Cited alongside, same era.
Demonstrating a Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
L. Smith, I. Kostrikov, and S. Levine · 2022
Cited alongside, same era.
RT-1: Robotics Transformer for Real-World Control at Scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al · 2022
Cited alongside, same era.
Do As I Can and Not As I Say: Grounding Language in Robotic Affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. J. Ruano, K. Jeffrey, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, K.-H. Lee, S. Levine, Y. Lu, L. Luu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Rettinghouse, D. Reyes, P. Sermanet, N. Sievers, C. Tan, A. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, S. Xu, M. Yan, and A. Zeng · 2022
Cited alongside, same era.
Later among the works it cites.
ALAN: Autonomously Exploring Robotic Agents in the Real World
R. Mendonca, S. Bahl, and D. Pathak · 2023
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 2023
Later among the works it cites.
Language to Rewards for Robotic Skill Synthesis
W. Yu, N. Gileadi, C. Fu, S. Kirmani, K.-H. Lee, M. G. Arenas, H.-T. L. Chiang, T. Erez, L. Hasenclever, J. Humplik, et al · 2023
Later among the works it cites.
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
H. Li, X. Yang, Z. Wang, X. Zhu, J. Zhou, Y. Qiao, X. Wang, H. Li, L. Lu, and J. Dai · 2023
Later among the works it cites.
Vision-Language Models as a Source of Rewards
K. Baumli, S. Baveja, F. Behbahani, H. Chan, G. Comanici, S. Flennerhag, M. Gazeau, K. Holsheimer, D. Horgan, M. Laskin, et al · 2023
Later among the works it cites.
N. M. M. Shafiullah, A. Rai, H. Etukuru, Y. Liu, I. Misra, S. Chintala, and L. Pinto · 2023
Later among the works it cites.
Multi-Skill Mobile Manipulation for Object Rearrangement
J. Gu, D. S. Chaplot, H. Su, and J. Malik · 2023
Later among the works it cites.
ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation
N. Yokoyama, A. Clegg, J. Truong, E. Undersander, T.-Y. Yang, S. Arnaud, S. Ha, D. Batra, and A. Rai · 2023
Later among the works it cites.
Efficient Online Reinforcement Learning with Offline Data
P. J. Ball, L. Smith, I. Kostrikov, and S. Levine · 2023
Later among the works it cites.
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
K. Stachowicz, D. Shah, A. Bhorkar, I. Kostrikov, and S. Levine · 2023
Later among the works it cites.
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, et al · 2023
Later among the works it cites.
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Z. Fu, T. Z. Zhao, and C. Finn · 2024
Closest in time.