Learning dexterous in-hand manipulation
Open accessAbstract
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM .
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Dynamics randomization can help acquire a robust policy, particularly where a controlling policy is needed.
“In addition to adding randomization to the visual input, dynamics randomization could also help acquire a robust policy particularly where the controlling policy is needed.”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [57]
Learning from demonstrations avoids per-task reward engineering but makes data the bottleneck, since contact-rich demonstrations are difficult to collect at scale.
“Learning from demonstrations is the prevailing route to such skills, avoiding the per-task reward engineering of reinforcement learning (Andrychowicz et al., 2020;Chen et al., 2022), but it shifts the bottleneck to data: contact-rich demonstrations are precisely what is hard to collect at scale.”
From Dexterous Robot Manipulation from Human Demonstrations via Contact-Anchored Retargeting and Residual Policy Learning (2026) · cites this paper as Andrychowicz et al., 2020
Domain randomization has a powerful effect, as shown by successful sim-to-real transfer experiments for dexterous in-hand manipulation with a physical five-fingered hand.
“Their successful sim-to-real transfer experiments show the powerful effect of domain randomization.”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [57]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
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 · see citing works
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
(1995) · Cited by 10,558 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767 · see citing works
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917710318 · Cited by 1,971 · see citing works
Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint
Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687 · see citing works
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989385 · Cited by 1,485 · see citing works
Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Aparicio, J., & Goldberg, K. Y. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.058 · Cited by 1,299 · see citing works
The GRASP Taxonomy of Human Grasp Types
Feix, T., Romero, J., Schmiedmayer, H., Dollar, A. M., & Kragić, D. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2470657 · Cited by 1,031 · see citing works
Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity
Bicchi, A. (2000) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.897777 · Cited by 899 · see citing works
CAD2RL: Real Single-Image Flight Without a Single Real Image
Sadeghi, F., & Levine, S. (2017) · Robotics: Science and Systems XIII · doi:10.15607/rss.2017.xiii.034 · Cited by 618 · see citing works
Robust adversarial reinforcement learning
Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017) · arXiv · doi:10.48550/arxiv.1703.02702 · Cited by 507 · cited 1× · see citing works
“[48] propose to use adversarial training to obtain more robust policies and show that it also helps with transfer to physical robots [47].”
BackgroundAn overview of dexterous manipulation
Okamura, A. M., Smaby, N., & Cutkosky, M. R. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.844067 · Cited by 480 · see citing works
An exploration of sensorless manipulation
Erdmann, M. A., & Mason, M. T. (1988) · IEEE Journal on Robotics and Automation · doi:10.1109/56.800 · Cited by 467 · see citing works
Sim-to-Real Robot Learning from Pixels with Progressive Nets
Rusu, A. A., Vecerik, M., Rothörl, T., Heess, N., Pascanu, R., & Hadsell, R. (2016) · arXiv · doi:10.48550/arxiv.1610.04286 · Cited by 385 · see citing works
Extrinsic dexterity: In-hand manipulation with external forces
Chavan-Dafle, N., Rodríguez, A., Paolini, R., Tang, B., Srinivasa, S. S., Erdmann, M. A., Mason, M. T., Lundberg, I., Staab, H., & Fuhlbrigge, T. A. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907062 · Cited by 276 · see citing works
Learning contact-rich manipulation skills with guided policy search
Levine, S., Wagener, N., & Abbeel, P. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7138994 · Cited by 276 · see citing works
Preparing for the unknown: Learning a universal policy with online system identification
Yu, W., Tan, J., Karen Liu, C., & Turk, G. (2017) · Robotics: Science and Systems XIII · doi:10.15607/rss.2017.xiii.048 · Cited by 237 · see citing works
Optimal control with learned local models: Application to dexterous manipulation
Kumar, V., Todorov, E. V., & Levine, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487156 · Cited by 207 · see citing works
Learning robot in-hand manipulation with tactile features
van Hoof, H., Hermans, T., Neumann, G. M., & Peters, J. (2015) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2015.7363524 · Cited by 162 · see citing works
On dexterity and dexterous manipulation
Ma, R. R., & Dollar, A. M. (2011) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2011.6088576 · Cited by 160 · see citing works
Pivoting: A new method of graspless manipulation of object by robot fingers
AIYAMA, Y., Inaba, M., & Inoue, H. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1993.583091 · Cited by 154 · see citing works
Dextrous manipulation by rolling and finger gaiting
Han, L., & Trinkle, J. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1998.677060 · Cited by 147 · see citing works
Dexterous manipulation through rolling
(2002) · doi:10.1109/robot.1995.525325 · Cited by 134 · see citing works
Dynamic In-Hand Sliding Manipulation
Shi, J. H., Woodruff, J. Z., Umbanhowar, P. B., et al. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2693391 · Cited by 125 · see citing works
Tournassoud, P., Lozano‐Pérez, T., & Mazer, E. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1987.1087910 · Cited by 110 · see citing works
Learning of grasp adaptation through experience and tactile sensing
Li, M., Bekiroglu, Y., Kragić, D., & Billard, A. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943027 · Cited by 107 · see citing works
Learning object-level impedance control for robust grasping and dexterous manipulation
Li, M., Yin, H., Tahara, K., & Billard, A. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907861 · Cited by 103 · see citing works
Dexterous manipulation using both palm and fingers
Bai, Y., & Liu, C. K. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907059 · Cited by 99 · see citing works
Dynamic object manipulation using a virtual frame by a triple soft-fingered robotic hand
Tahara, K., Arimoto, S., & Yoshida, M. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509372 · Cited by 94 · see citing works
Implementing a force strategy for object re-orientation
Fearing, R. S. (1986) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1986.1087655 · Cited by 93 · see citing works
Dextrous manipulation with rolling contacts
Han, L., Guan, Y., Li, Z., et al. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1997.614264 · Cited by 85 · see citing works
An Exploration of Nonprehensile Two-Palm Manipulation
Erdmann, M. A. (1998) · The International Journal of Robotics Research · doi:10.1177/027836499801700502 · Cited by 85 · see citing works
In-Hand Dexterous Manipulation of Piecewise-Smooth 3-D Objects
Rus, D. L. (1999) · The International Journal of Robotics Research · doi:10.1177/02783649922066268 · Cited by 78 · see citing works
Planning quasi-static fingertip manipulations for reconfiguring objects
Cherif, M., & Gupta, K. K. (1999) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.795789 · Cited by 72 · see citing works
Supervision via competition: Robot adversaries for learning tasks
Pinto, L., Davidson, J., & Gupta, A. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989190 · Cited by 63 · see citing works
Tumbling Objects Using a Multi-fingered Robot.
Sawasaki, N., & Inoue, H. (1991) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.9.560 · Cited by 57 · see citing works
Mechanics, Planning, and Control for Tapping
Huang, W. H., & Mason, M. T. (2000) · The International Journal of Robotics Research · doi:10.1177/02783640022067841 · Cited by 46 · see citing works
In-Hand Manipulation in Young Children: Rotation of an Object in the Fingers
Pehoski, C., Henderson, A., & Tickle‐Degnen, L. (1997) · American Journal of Occupational Therapy · doi:10.5014/ajot.51.7.544 · Cited by 43 · see citing works
Stably supported rotations of a planar polygon with two frictionless contacts
Abell, T., & Erdmann, M. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1995.525918 · Cited by 38 · see citing works
Dexterous rotations of polyhedra
Rus, D. L. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1992.220017 · Cited by 31 · see citing works
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation
Falco, P., Attawia, A., Saveriano, M., & Lee, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800110 · Cited by 29 · see citing works
On rolling contact motion by robotic fingers via prescribed performance control
Doulgeri, Z., & Δρούκας, Λ. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631137 · Cited by 22 · see citing works
Rotary object dexterous manipulation in hand: a feedback-based method
Li, Q., Meier, M., Haschke, R., et al. (2013) · International Journal of Mechatronics and Automation · doi:10.1504/ijma.2013.052624 · Cited by 16 · see citing works
On Policy Learning Robust to Irreversible Events: an Application to Robotic In-Hand Manipulation
Falco, P. ;. A. (2018) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809 · see citing works
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Zhao, W., Queralta, J. P., & Westerlund, T. (2020) · 2020 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308468 · Cited by 941 · see citing works
“To learn dexterous in-hand manipulation policies for a physical five-fingered hand, [57] randomizes various physical parameters in the simulator, such as object dimensions, objects and robot link masses, surface friction coefficients, robot joint damping coefficients and actuator force gains.”
MethodElectronic skins and machine learning for intelligent soft robots
Shih, B., Shah, D., Li, J., Thuruthel, T. G., Park, Y.-L., Iida, F., Bao, Z., Kramer-Bottiglio, R., & Tolley, M. T. (2020) · Science Robotics · doi:10.1126/scirobotics.aaz9239 · Cited by 715 · see citing works
Unity: A General Platform for Intelligent Agents
Juliani, A., Berges, V.-P., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., et al. (2018) · arXiv · doi:10.48550/arxiv.1809.02627 · Cited by 626 · see citing works
“Recent work at OpenAI has also taken advantage of the rendering capabilities of the Unity engine to aid in the development of a system used to transfer a robotic hand’s grasping policy from a simulator to a physical robot (Andrychowicz et al., 2018).”
BackgroundLearning Agile Robotic Locomotion Skills by Imitating Animals
Bin Peng, X., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., & Levine, S. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.064 · Cited by 357 · see citing works
Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
Sharma, S., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2999148 · Cited by 196 · see citing works
On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward
Choi, H., Crump, C., Duriez, C., Elmquist, A., Hager, G., Han, D., Hearl, F., Hodgins, J., Jain, A., Leve, F., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907856118 · Cited by 162 · see citing works
Loss of plasticity in deep continual learning
Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172 · see citing works
“Machine learning and artificial intelligence have made remarkable progress in the past decade, with landmark successes in natural-language processing 2,3 , biology 4 , game playing 5–8 and robotics 9,10 .”
Seita, D., Florence, P., Tompson, J., Coumans, E., Sindhwani, V., Goldberg, K., & Zeng, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561391 · Cited by 130 · see citing works
Navigating to objects in the real world
Gervet, T., Chintala, S., Batra, D., Malik, J., & Chaplot, D. S. (2023) · Science Robotics · doi:10.1126/scirobotics.adf6991 · Cited by 125 · see citing works
Silver, T., Allen, K., Tenenbaum, J., & Kaelbling, L. (2018) · arXiv · doi:10.48550/arxiv.1812.06298 · Cited by 113 · see citing works
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
Guss, W. H., Houghton, B., Topin, N., Wang, P., Codel, C., Veloso, M., & Salakhutdinov, R. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/339 · Cited by 94 · see citing works
Ditto: Building Digital Twins of Articulated Objects from Interaction
Jiang, Z., Hsu, C.-C., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00553 · Cited by 92 · see citing works
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Nasiriany, S., Liu, H., & Zhu, Y. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9812140 · Cited by 91 · see citing works
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.034 · Cited by 74 · see citing works
Sim-to-Real Transfer for Optical Tactile Sensing
Ding, Z., Lepora, N. F., & Johns, E. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197512 · Cited by 53 · see citing works
Industrial Transfer Learning: Boosting Machine Learning in Production
Tercan, H., Guajardo, A., & Meisen, T. (2019) · 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972099 · Cited by 50 · see citing works
Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
Lee, R., Ward, D., Cosgun, A., Dasagi, V., Corke, P., & Leitner, J. (2020) · arXiv · doi:10.48550/arxiv.2010.03209 · Cited by 47 · see citing works
DayDreamer: World Models for Physical Robot Learning
Wu, P., Escontrela, A., Hafner, D., Goldberg, K., & Abbeel, P. (2022) · arXiv · doi:10.48550/arxiv.2206.14176 · Cited by 76 · see citing works
Visuospatial foresight for physical sequential fabric manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A. K., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2022) · Autonomous Robots · doi:10.1007/s10514-021-10001-0 · Cited by 45 · see citing works
Replay-Guided Adversarial Environment Design
Jiang, M., Dennis, M., Parker-Holder, J., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv · doi:10.48550/arxiv.2110.02439 · Cited by 21 · see citing works
Lyapunov-Based Reinforcement Learning for Decentralized Multi-agent Control
Zhang, Q., Dong, H., & Pan, W. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-64096-5_5 · Cited by 18 · see citing works
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation
Zhao, C., & Hospedales, T. (2020) · arXiv · doi:10.48550/arxiv.2012.04839 · Cited by 11 · see citing works
Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
Kim, S., Sorokin, M., Lee, J., & Ha, S. (2022) · Robotics: Science and Systems XVIII · doi:10.15607/rss.2022.xviii.021 · Cited by 9 · see citing works
Procedural Generalization by Planning with Self-Supervised World Models
Anand, A., Walker, J., Li, Y., Vértes, E., Schrittwieser, J., Ozair, S., Weber, T., & Hamrick, J. B. (2021) · arXiv · doi:10.48550/arxiv.2111.01587 · Cited by 5 · see citing works
Evaluating Real-World Robot Manipulation Policies in Simulation
Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H. R., Fu, C., Lunawat, I., Sieh, I., Kirmani, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.05941 · Cited by 4 · see citing works
“When choosing aspects of the simulation problem to focus on, we take inspiration from the rich literature on sim-to-real policy learning [2, 52, 64, 75].”
Towards Practical Credit Assignment for Deep Reinforcement Learning
Alipov, V., Simmons-Edler, R., Putintsev, N., Kalinin, P., & Vetrov, D. (2021) · arXiv · doi:10.48550/arxiv.2106.04499 · Cited by 3 · see citing works
Yang, Z., Liu, C., Zhou, Y., Lv, R., Cui, T., Yi, S., Zhu, H., Lu, I., & Sun, J. (2026) · arXiv · doi:10.48550/arxiv.2609.24093
“Sim‑to‑real reinforcement learning (Andrychowicz et al., 2020; Peng et al., 2018) avoids real‑robot training, and real2sim2real pipelines reconstruct demonstrations in simulation, refine policies there, and transfer back.”
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Teoh, J., Li, W., & Varakantham, P. (2025) · arXiv
“Although recent advancements in Deep Reinforcement Learning (DRL) have led to many successes, e.g., super-human performance in games [26, 9] and reliable control in robotics [2, 3], training generally-capable agents remains a significant challenge.”
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Teoh, J., Li, W., & Varakantham, P. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-4300
- Authors
- Andrychowicz, O. M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., Schneider, J., Sidor, S., Tobin, J., Welinder, P., Weng, L., & Zaremba, W.
- Affiliations
- OpenAI (United States) (US)
- OpenAI, San Francisco, CA, USA
- Corresponding author
- Plappert, Matthias
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2019-11-18
- Volume
- 39
- Issue
- 1
- Pages
- 3-20
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2990747716
- Cited by
- 1,643
- References
- 48
- Citations by year
- 2026: 120 · 2025: 224 · 2024: 221 · 2023: 243 · 2022: 183 · 2021: 263 · 2020: 244 · 2019: 137 · 2018: 8
- FWCI
- 93.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The International Journal of Robotics Research (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- in-hand manipulation, dexterous manipulation, object reorientation, simulation-to-robot transfer, visual servoing, distributed reinforcement learning, human-robot interaction
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
