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In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments that remain beyond the reach of current robots.
J. K. Salisbury and J. J. Craig, “Articulated hands: Force control and kinematic issues,” The International journal of Robotics research , vol. 1, no. 1, pp. 4–17, 1982
1982
Earlier work this paper cites.
M. T. Mason, J. K. Salisbury, and J. K. Parker, Robot hands and the mechanics of manipulation . The MIT Press, 1989
1989
Earlier work this paper cites.
J. L. Hintze and R. D. Nelson, “Violin plots: a box plot-density trace synergism,” The American Statistician , vol. 52, no. 2, pp. 181–184, 1998
1998
Earlier work this paper cites.
D. Rus, “In-hand dexterous manipulation of piecewise-smooth 3-d objects,” The International Journal of Robotics Research , vol. 18, no. 4, pp. 355–381, 1999
1999
Earlier work this paper cites.
A. Y. Ng, D. Harada, and S. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” in Proceedings of the Sixteenth International Conference on Machine Learning , vol. 99, 1999, pp. 278–287
1999
Earlier work this paper cites.
K. Daniilidis, “Hand-eye calibration using dual quaternions,” The International Journal of Robotics Research , vol. 18, no. 3, pp. 286–298, 1999
1999
Earlier work this paper cites.
N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es),” Evolutionary computation , vol. 11, no. 1, pp. 1–18, 2003
2003
Earlier work this paper cites.
N. Furukawa, A. Namiki, S. Taku, and M. Ishikawa, “Dynamic regrasping using a high-speed multifingered hand and a high-speed vision system,” in Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE, 2006, pp. 181–187
2006
Earlier work this paper cites.
T. Ishihara, A. Namiki, M. Ishikawa, and M. Shimojo, “Dynamic pen spinning using a high-speed multifingered hand with high-speed tactile sensor,” in 6th IEEE-RAS International Conference on Humanoid Robots . IEEE, 2006, pp. 258–263
2006
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al. , “ROS: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5
2009
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635
2011
Earlier work this paper cites.
I. Mordatch, Z. Popović, and E. Todorov, “Contact-invariant optimization for hand manipulation,” in Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation , 2012, pp. 137–144
2012
Earlier work this paper cites.
Y. Bai and C. K. Liu, “Dexterous manipulation using both palm and fingers,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 1560–1565
2014
Earlier work this paper cites.
V. Kumar, Y. Tassa, T. Erez, and E. Todorov, “Real-time behaviour synthesis for dynamic hand-manipulation,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 6808–6815
2014
Earlier work this paper cites.
N. C. Dafle, A. Rodriguez, R. Paolini, B. Tang, S. S. Srinivasa, M. Erdmann, M. T. Mason, I. Lundberg, H. Staab, and T. Fuhlbrigge, “Extrinsic dexterity: In-hand manipulation with external forces,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 1578–1585
2014
Earlier work this paper cites.
K. Cho, B. van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Oct. 2014, pp. 1724–1734
2014
Earlier work this paper cites.
H. Van Hoof, T. Hermans, G. Neumann, and J. Peters, “Learning robot in-hand manipulation with tactile features,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) . IEEE, 2015, pp. 121–127
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Mamou, E. Lengyel, and A. Peters, “Volumetric hierarchical approximate convex decomposition,” in Game Engine Gems 3 . AK Peters, 2016, pp. 141–158
2016
Cited alongside, same era.
D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus),” 4th International Conference on Learning Representations (ICLR) , 2016
2016
Cited alongside, same era.
B. Calli and A. M. Dollar, “Vision-based model predictive control for within-hand precision manipulation with underactuated grippers,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 2839–2845
2017
Cited alongside, same era.
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine, “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” Robotics: Science and Systems (RSS) , 2017
2017
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Later among the works it cites.
M. Ahn, H. Zhu, K. Hartikainen, H. Ponte, A. Gupta, S. Levine, and V. Kumar, “Robel: Robotics benchmarks for learning with low-cost robots,” in Conference on Robot Learning . PMLR, 2020, pp. 1300–1313
2020
Later among the works it cites.
A. Bhatt, A. Sieler, S. Puhlmann, and O. Brock, “Surprisingly robust in-hand manipulation: An empirical study,” Robotics: Science and Systems (RSS) , 2021
2021
Later among the works it cites.
A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” Robotics: Science and Systems (RSS) , 2021
2021
Later among the works it cites.
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Cited alongside, same era.
2017
Cited alongside, same era.
B. Calli, A. Kimmel, K. Hang, K. Bekris, and A. Dollar, “Path planning for within-hand manipulation over learned representations of safe states,” in International Symposium on Experimental Robotics . Springer, 2018, pp. 437–447
2018
Cited alongside, same era.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots,” Robotics: Science and Systems (RSS) , 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
B. Sundaralingam and T. Hermans, “Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation,” Autonomous Robots , vol. 43, no. 2, pp. 469–483, 2019
2019
Cited alongside, same era.
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3651–3657
2019
Cited alongside, same era.
S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “ContactDB: Analyzing and predicting grasp contact via thermal imaging,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
C. Chen, P. Culbertson, M. Lepert, M. Schwager, and J. Bohg, “Trajectotree: Trajectory optimization meets tree search for planning multi-contact dexterous manipulation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8262–8268
2021
Later among the works it cites.
J. Xu, T. Chen, L. Zlokapa, M. Foshey, W. Matusik, S. Sueda, and P. Agrawal, “An end-to-end differentiable framework for contact-aware robot design,” Robotics: Science and Systems , 2021
2021
Later among the works it cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance GPU based physics simulation for robot learning,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2021
2021
Later among the works it cites.
T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object reorientation,” in Conference on Robot Learning . PMLR, 2022, pp. 297–307
2022
Closest in time.
G. Khandate, M. Haas-Heger, and M. Ciocarlie, “On the feasibility of learning finger-gaiting in-hand manipulation with intrinsic sensing,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2752–2758
2022
Closest in time.
A. S. Morgan, K. Hang, B. Wen, K. Bekris, and A. M. Dollar, “Complex in-hand manipulation via compliance-enabled finger gaiting and multi-modal planning,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4821–4828, 2022
2022
Closest in time.
L. Sievers, J. Pitz, and B. Bäuml, “Learning purely tactile in-hand manipulation with a torque-controlled hand,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 2745–2751
2022
Closest in time.
A. Allshire, M. MittaI, V. Lodaya, V. Makoviychuk, D. Makoviichuk, F. Widmaier, M. Wüthrich, S. Bauer, A. Handa, and A. Garg, “Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 802–11 809
2022
Closest in time.
G. B. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. Kim, and P. Agrawal, “Learning to jump from pixels,” in Conference on Robot Learning . PMLR, 2022, pp. 1025–1034
2022
Closest in time.
2022
Closest in time.
L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V. Vanhoucke, “Google scanned objects: A high-quality dataset of 3d scanned household items,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2553–2560
2022
Closest in time.
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “Implicit behavioral cloning,” in Conference on Robot Learning . PMLR, 2022, pp. 158–168
2022
Closest in time.
A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam et al. , “Dextreme: Transfer of agile in-hand manipulation from simulation to reality,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5977–5984
2023
Closest in time.