The DOI matches its Crossref record; 184 of the 193 references it cites were found in a registry or an index.
How to train your robot with deep reinforcement learning: lessons we have learned
Open accessAbstract
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“POMDPs more accurately describe real-world applications of robotic systems [7] and are a major tool for training modern DRL and DIL end-to-end policy controllers.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, DRL is data-hungry and trained through trial and error which requires human involvement for constantly resetting the environment and preventing unsafe actions [7].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“(4) high-dimensional/continuous state settings exacerbate the challenge of exploration and further affect data efficiency.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We can also utilise demonstration data to further reduce the distribution shift, as it causes the model to learn the important part of the state space [7].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In recent years, data-driven approaches with deep learning (DL) [6], such as deep reinforcement learning (DRL) [7] and deep imitation learning (DIL) [8], have demonstrated many advantages in performing task-level and skill-level control problems (Section 3.3).”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“(3) DRL algorithms often come with a set of hyper-parameters, and the algorithms are often sensitive to these hyper-parameters and the application domain”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“(2) data efficiency (both sampling and learning efficiency) is a major concern for such complex settings”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“MBRL is generally more data-efficient than MFRL algorithms and it generalises better with large and diverse data [7].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“(1) theoretically, we have no guarantee of convergence of the DRL algorithms”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [7]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
(2017) · Cited by 11,625
Research works printed but not linked: 61
- Achiam J, 2017, International Conference on Machine Learning
- Agarwal R, 2020, International Conference on Machine Learning
- Andrychowicz M, 2017, Advances in Neural Information Processing Systems, 5048
- Burda Y, 2019, International Conference on Learning Representations
- Cabi S, 2019, arXiv preprint arXiv:1909.12200 · arXiv
- Chebotar Y, 2017, International Conference on Machine Learning, 703
- Chen Z, 2018, International Conference on Machine Learning
- Clavera I, 2019, International Conference on Learning Representations
- Deisenroth M, 2011, International Conference on Machine Learning, 465
- Deisenroth MP, 2013, Foundations and Trends in Robotics, 2, 1
- Devlin J, 2019, NAACL-HLT
- Duan Y, 2017, Advances in Neural Information Processing Systems, 1087
- Eysenbach B, 2018, International Conference on Learning Representations
- Finn C, 2017, International Conference on Machine Learning
- Finn C, 2016, International Conference on Machine Learning, 49
- Finn C, 2017, Proceedings of Machine Learning Research, 78, 357
- Fox R, 2016, Conference on Uncertainty in Artificial Intelligence
- Fu J, 2017, Advances in Neural Information Processing Systems, 2577
- Fujimoto S, 2019, International Conference on Machine Learning
- Fujimoto S, 2018, International Conference on Machine Learning
- Garofolo JS, 1993, NASA STI/Recon Technical Report, 93
- Goodfellow I, 2014, Advances in Neural Information Processing Systems, 2672
- Gregor K, 2017, International Conference on Learning Representations, Workshop Track Proceedings
- Gu S, 2016, International Conference on Machine Learning, 2829
- Haarnoja T, 2017, International Conference on Machine Learning, 1352
- Haarnoja T, 2018, International Conference on Machine Learning
- Hausman K, 2017, AAAI Symposium on Interactive Multi-Sensory Object Perception for Embodied Agents
- Iscen A, 2018, Conference on Robot Learning
- Jabri A, 2019, Advances in Neural Information Processing Systems, 10519
- James S, 2018, Conference on Robot Learning
- James S, 2017, Conference on Robot Learning
- Kalashnikov D, 2018, Conference on Robot Learning, Proceedings of Machine Learning Research
- Khadka S, 2019, International Conference on Machine Learning
- Kroemer O, 2019, CoRR abs/1907.03146 · arXiv
- Kumar A, 2019, Advances in Neural Information Processing Systems, 32
- Kurutach T, 2018, International Conference on Learning Representations
- Levine S, 2014, Advances in Neural Information Processing Systems, 1071
- Levine S, 2016, The Journal of Machine Learning Research, 17, 1334
- Levine S, 2013, International Conference on Machine Learning
- Mania H, 2018, Advances in Neural Information Processing Systems
- Montgomery WH, 2016, Advances in Neural Information Processing Systems, 4008
- Nagabandi A, 2020, Conference on Robot Learning
- Osband I, 2016, Advances in Neural Information Processing Systems, 4026
- Parisotto E, 2016, CoRR
- Paszke A, 2017, Advances in Neural Information Processing Systems Workshop on Autodiff
- Pinto L, 2017, International Conference on Machine Learning
- Rakelly K, 2019, International Conference on Machine Learning
- Riedmiller M, 2018, International Conference on Machine Learning
- Schaul T, 2019, Multidisciplinary Conference on Reinforcement Learning and Decision Making
- Schoettler G, 2019, International Conference on Intelligent Robots and Systems
- Schulman J, 2015, International Conference on Machine Learning
- Sener O, 2018, Advances in Neural Information Processing Systems, 527
- Tedrake R, 2015, Workshop on Adaptive and Learning Systems
- Viereck U, 2017, Conference on Robot Learning
- Wu YH, 2019, International Conference on Machine Learning, Proceedings of Machine Learning Research
- Xiao T, 2020, International Conference on Learning Representations
- Xie A, 2019, arXiv preprint arXiv:1904.05538 · arXiv
- Xie A, 2018, Proceedings of Machine Learning Research, 87, 40
- Yang Y, 2019, AAMAS
- Yang Y, 2020, Conference on Robot Learning
- Yen-Chen L, 2020, Conference on Robot Learning
Cited by
Sorted by citations
Items 501–569 of 569
Chen, S. H., Wu, P.-S., Soon, B. D., Chen, C.-H., Liu, C., Hsu, C., & Yang, C. (2025) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2025.3626988
Mitigating Q-Value Overestimation through Latent Causal Modeling in Deep Reinforcement Learning
Cai, R., Lin, F., Chen, W., Qiao, J., Zhu, H., & Hao, Z. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228998
Tactics, Techniques, and Procedures for Improving Robot Utilization in Healthcare Monitoring
Sakr, H. A., Elnahas, M., Mohamed, Y., Mohamed, A., Ashraf, S. H., Girgis, P., Gaber, T., Mohamed, K., Mohamed, A., Akmal, N., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-09562-6_6
Fülöp, T., & Katona, J. (2025) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-025-00621-5 · Cited by 1
Exploring the Basics of Reinforcement Learning
Rattan, P., Nair, R. K. R., & Cengiz, K. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815322316125010004
FMMDP: failure monitoring approach for DNN-based Markov decision process
Cai, Y., Lin, W., Jing, C., Liu, Z., & Zheng, Z. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10757-4
Xu, B., Yijun, H. E., & Xu, Y. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122985
A Bipedal Walking Robot with Perception-Control Integrated Ankle Joints
Yang, H., Li, B., Liu, L., Yuan, Z., Zhou, L., Li, X., & Chen, R. (2025) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs65871.2025.11267736
Applied Reinforcement Learning: An Implementation Roadmap
Laflamme, C., Viharos, Z. J., Pálvölgyi, B., Schaefer, J., & Haeussler, S. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.12.024
Kotani, N. (2025) · Electronics and Communications in Japan · doi:10.1002/ecj.70003
Latent Behavior Cloning for Deep Reinforcement Learning Robotic Tasks
Voulgarakis, R., Katsikas, D., Passalis, N., & Tefas, A. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226088
What is Balance? A Vital Mechano-Regulation Paradigm
Wilkinson, N. M. (2025) · Open Mind · doi:10.1162/opmi.a.257
Shen, C., & Sloth, C. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1682200
Reinforcement Learning-Driven Adaptive Decision Framework for Autonomous Driving
Lino, & Bell, J. B. B. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11331298
Kumar, S., Mallampati, D., Rasveen, Sudhakar, M., Sabitha, B., & Choudhary, S. (2025) · IEEE International Conference on Advanced Computing Technologies (ICACT) · doi:10.1109/icact67549.2025.11351421
Autonomous navigation via dual-priority experience replay with adaptive hybrid weighting
Zhang, Y., Xiao, W., Yuan, L. Z., Ran, T., Cui, J., & Lv, K. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00693-7
Towards Smarter Robotics: AI-Powered Potential Fields for Obstacle Avoidance
Miandoab, E. M., Mozaffari, S., & Alirezaee, S. (2025) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece64018.2025.11364381
Zhang, Q., Li, G., Liu, B., Li, C., Zhu, C., & Chai, H. (2026) · Biomimetics · doi:10.3390/biomimetics11020115 · Cited by 1
Haptic-Enhanced Imitation Learning System for Soft Object ManipulationPreprint
Uthai, T., You, H., Zhou, T., & Du, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6186920
Noh, S., Kim, S., & Jang, I. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11388104
Miao, C., Sun, M., Jiang, Y., Chen, Y., & Cui, Y. (2025) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio66223.2025.11378324
Ng, C. K., Gao, H., Ren, T.-A., Lai, J., & Ren, H. (2025) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio66223.2025.11376000
Bastani, N., Bigdeli, N., & Zarabadipour, H. (2026) · Asian Journal of Control · doi:10.1002/asjc.70076
Freeman, A., & Bauer, R. (2026) · International Journal of Advanced Robotic Systems · doi:10.1177/17298806261431893
Reinforcement Learning-Based Whole-Body Motion Control for Humanoids With Position-Controlled Joints
Liu, C., Xiao, K., Hu, M., Fang, Q. Z., Xu, Z., & Chen, Q. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3673779
Hu, J., Zhai, P., Wei, X., Liu, C., & Zhang, L. (2026) · Robotica · doi:10.1017/s0263574726103300
MRISA: A Visual Analytics Approach of Locomotion Policies Comparison for Robotics Training
Li, N., Shi, F., Zhang, X., & Wang, A. Y. (2025) · Graphics Interface · doi:10.1145/3769872.3769898
S$^{3}$aDPWo: Spatial-, Semantic-, and Shape-Aware Diffusion Policy Toward Autonomous Wound Repair
Xu, W., Fang, H., Cao, Z., Tan, Z., Wang, G., Wang, H., & Wang, W. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3681101
Experimental data assimilation : learning-based estimation for state-space models
Buisson-Fenet, M. (2023) · theses.fr (ABES) · doi:10.70675/5b2922d4z71acz4987zb74cz5ef1af72790b
Learning to Grasp in Robotics with Quality-Diversity Generated Data
Huber, J. B. (2024) · theses.fr (ABES) · doi:10.70675/b98f392dz4df9z4646z8b34z59f447564543
Improving Cooperation in Multi-agent Deep Reinforcement Learning for Mobile Robotics
Toquebiau, M. (2024) · theses.fr (ABES) · doi:10.70675/3385d0bbzc66cz4fceza9e0z8696ff61ff4e
Deep Reinforcement Learning and Learning from Demonstrations for Robot Manipulators
Martín, J. B. (2024) · theses.fr (ABES) · doi:10.70675/dcf4fecbz66a7z4eecz8fc6z489111a62a52
Reinforcement Learning for Controlling Cable Driven Parallel Robots
Bouaouda, A. (2024) · theses.fr (ABES) · doi:10.70675/645e165ez0ef6z4d2cz9dd0zeaac240529a6
Self-Evolved Imitation Learning in Simulated World
Ye, Y., Cen, J., Chen, J., & Lu, Z. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3683594 · Cited by 5
TCN-SAC: Enhancing the Soft Actor-Critic Algorithm with Temporal Convolutional Networks
Zhou, Y., Yang, J., Li, Z., & Qiu, J. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487489
Trust the Similar Ones: Belief-Based Q-Learning with Embedding Proximity
Wang, C. Z. (2026) · International Conference on Artificial Intelligence and Control (ICAIC) · doi:10.1145/3807246.3807315
Bu, Z., Chen, X., Tang, Y., & Qu, R. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261441359
Sim2Real Transfer for End-to-End Dynamic Robotic Grasping via Domain Randomization
Luo, Z., Xie, W., Jiang, L., Luo, J., Lin, J., & Tang, J. (2026) · International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM) · doi:10.1109/icmtim69588.2026.11525620
DexAnyTwist: Learning general dexterous twisting with hybrid manipulation system identification
Liu, X., Dong, Y., Wan, J., Deng, L., Hua, F., Shen, Y., Yu, M., Ma, G., Cheng, C., Song, H., et al. (2026) · National Science Review · doi:10.1093/nsr/nwag351
Haircutting robots: a mobile robotics perspective
Huang, Z., Khan, A. T., & Li, S. (2026) · International Journal of Systems Science · doi:10.1080/00207721.2026.2687894
Research on Path Planning for AI Robots Using Deep Reinforcement Learning Algorithms
Xu, X. (2026) · International Symposium on Robotics, Artificial Intelligence and Information Engineering (RAIIE) · doi:10.1109/raiie70320.2026.11566830
Co-Optimizing Physical Reconfiguration Parameters and Controllers for a Reconfigurable Manipulator
Chen, Z., Chen, L., Zhang, H., & Zhao, J. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2026.3700119
Jin, Z., Wang, H., Tang, Y., Dong, Z., Feng, C., & Shao, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7029004
Singh, M., Singh, S., Tyagi, K. D., & Tyagi, V. B. (2026) · De Gruyter eBooks · doi:10.1515/9783111633909-028
Social Learning-Enhanced Deep Reinforcement Learning Through Behavioral Observation
Erbaş, M. D., & Gülen, C. (2026) · Electronics · doi:10.3390/electronics15132940
Mou, W., & Zhu, Y. (2026) · SIAM Journal on Mathematics of Data Science · doi:10.1137/24m170199x
Robot-assisted rehabilitation: Collaborating a smart avatar with an AAN controller
Goyal, T., Hussain, F., Jamwal, P. K., & Hussain, S. (2026) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119261467479
Dual variational autoencoders for efficient sim-to-real transfer in low-cost robotic navigation
Díez, Á., & Aznar, F. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.106121
Task-Driven State Representation for Long-Horizon World Model
Zhao, T., Huang, J., He, Z., Zhang, H., Liu, J., & Wang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3381-6_48
From Trajectories to Instructions: Language-Conditioned Meta-reinforcement Learning
Singla, G., Maheswari, N. U., & Diddigi, R. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38404-1_8
Compensating for Kinematic Drift in X-Drive Mobile Robot Using Model-Free Reinforcement Learning
Aryanto, A. S., Ataka, A., Ardiyanto, I., & Zulfa, M. I. (2026) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con68836.2026.11608427
Chen, Y., Zhang, B., & Liu, C. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae8f62
Reinforcement Learning-Based Motion Planning and Adaptive Control Method for Robotic Manipulators
Wen, J. (2026) · International Conference on Computer Intelligence and Software Engineering (CICSE) · doi:10.1109/cicse70286.2026.11620711
Sonohara, S., Fujii, Y., Mori, Y., & Shimada, N. (2026) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur69537.2026.11626720
HSTGT-Nav: Relation-Aware Spatial-Temporal Evidence Routing for Socially Compliant Robot NavigationPreprint
Ao, T., Li, Z., Shi, H., Li, H., & Shi, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7231479
HSTGT-Nav: Relation-Aware Spatial-Temporal Evidence Routing for Socially Compliant Robot NavigationPreprint
Ao, T., Li, Z., Shi, H., Li, H., & Shi, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7231481
Artificial Intelligence in Orthodontics: A Scoping Review of Methods and Applications
Satravaha, Y., Chaweewannakorn, C., Sipiyaruk, K., Boonpratham, S., Likitkulthanaporn, A., Peanchitlertkajorn, S., & Aung, Z. H. (2026) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.70170
Combining exploration and imitation in contact-rich task learning on an articulated soft robot arm
Elstner, L., Kyrkjebø, E., & Stoelen, M. F. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2026.1885625
Generating Stall Recovery Policies Using Generalized Proximal Policy Optimization
Wang, J., Liu, T., & Tan, J. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-981-92-3082-2_22
Self-citationAuto-conditioned stochastic recursive variance reduced policy gradient method
Qin, J., Liu, L., & Liu, Y. (2026) · Numerical Algorithms · doi:10.1007/s11075-026-02472-y
Zhang, H., Zhang, M., Cao, Z., Zhang, S., Jing, C., Wang, J., & Zhang, H. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105741
Jarrah, K., & Rawashdeh, O. A. (2026) · Aerospace · doi:10.3390/aerospace13090825
Stegner, L. (2026) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3812677
A Fear Emotion-Based Cognitive Mechanism for Danger Avoidance Learning in Maze Robots
Zhang, X., & Guo, Y. (2026) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber71431.2026.11695558
Fan, R., Wang, X., Shao, G., Tang, J., Wang, Y., & Xu, P. (2026) · Sensors · doi:10.3390/s26196134
Energy Optimization System of Electric Vehicles for Real-World Deep Reinforcement Learning*
Xin, B. (2026) · IEEE International Conference on Compatibility, Power Electronics and Power Engineering · doi:10.1109/cpe-powereng67301.2026.11704738
Goal-Conditioned Self-Supervised Learning from Play Aimed at Human–Robot Collaboration
Hiramatsu, S., & Murata, S. (2026) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2026.p1402
Scheduling Techniques in Astronomical Observation: Survey, Taxonomy, Challenges, and Future Trends
Zhang, Y., Yu, C., Sun, C., Hu, Y., Shang, Z., Wei, J., Zhang, S., & Ju, J. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70138
Metadata
- Authors
- Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S.
- Affiliations
- Google (United States) (US)
- Everyday Robots, X, The Moonshot Factory, Mountain View, CA, USA
- Corresponding author
- Ibarz, Julian
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2021-01-31
- Volume
- 40
- Issue
- 4-5
- Pages
- 698-721
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W3126321819
- Cited by
- 570
- References
- 193
- Citations by year
- 2026: 63 · 2025: 123 · 2024: 145 · 2023: 112 · 2022: 89 · 2021: 38
- FWCI
- 33.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The International Journal of Robotics Research (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- deep reinforcement learning, robot learning, embodied agents, physical robots, robot skill learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
