FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy

E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 5026–5033

2012

Earlier work this paper cites.

WonikRobotics, “Allegrohand,” https://www.wonikrobotics.com/ , 2013

2013

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

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,” arXiv preprint arXiv:1709.10087 , 2017

Original

2017

Earlier work this paper cites.

H. Liu, X. Xie, M. Millar, M. Edmonds, F. Gao, Y. Zhu, V. J. Santos, B. Rothrock, and S.-C. Zhu, “A glove-based system for studying hand-object manipulation via joint pose and force sensing,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 6617–6624

2017

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Earlier work this paper cites.

D. Antotsiou, G. Garcia-Hernando, and T.-K. Kim, “Task-oriented hand motion retargeting for dexterous manipulation imitation,” in Proceedings of the European conference on computer vision (ECCV) workshops , 2018, pp. 0–0

2018

Earlier work this paper cites.

S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba, and W. Matusik, “Learning the signatures of the human grasp using a scalable tactile glove,” Nature , vol. 569, no. 7758, pp. 698–702, 2019

2019

Earlier work this paper cites.

O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020

2020

Earlier work this paper cites.

J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502 , 2020

Original

2020

Earlier work this paper cites.

Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang, “Dexmv: Imitation learning for dexterous manipulation from human videos,” in European Conference on Computer Vision . Springer, 2022, pp. 570–587

2022

Earlier work this paper cites.

A. Hammoud, V. Belcamino, A. Carfi, V. Perdereau, and F. Mastrogiovanni, “In-hand manipulation planning using human motion dictionary,” in 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) . IEEE, 2022, pp. 927–933

2022

Earlier work this paper cites.

Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” arXiv preprint arXiv:2210.02747 , 2022

Original

2022

Earlier work this paper cites.

C. Wang, X. Luo, K. Ross, and D. Li, “Vrl3: A data-driven framework for visual deep reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 32 974–32 988, 2022

2022

Cited alongside, same era.

K. Zhou, B. L. Bhatnagar, J. E. Lenssen, and G. Pons-Moll, “Toch: Spatio-temporal object-to-hand correspondence for motion refinement,” in European Conference on Computer Vision . Springer, 2022, pp. 1–19

2022

Cited alongside, same era.

K. Xu, Z. Hu, R. Doshi, A. Rovinsky, V. Kumar, A. Gupta, and S. Levine, “Dexterous manipulation from images: Autonomous real-world rl via substep guidance,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5938–5945

2023

Cited alongside, same era.

Z.-H. Yin, B. Huang, Y. Qin, Q. Chen, and X. Wang, “Rotating without seeing: Towards in-hand dexterity through touch,” arXiv preprint arXiv:2303.10880 , 2023

Original

2023

Cited alongside, same era.

I. Guzey, Y. Dai, B. Evans, S. Chintala, and L. Pinto, “See to touch: Learning tactile dexterity through visual incentives,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 13 825–13 832

2024

Later among the works it cites.

Y. Yuan, H. Che, Y. Qin, B. Huang, Z.-H. Yin, K.-W. Lee, Y. Wu, S.-C. Lim, and X. Wang, “Robot synesthesia: In-hand manipulation with visuotactile sensing,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6558–6565

2024

Later among the works it cites.

C. Jiang, W. Xu, Y. Li, Z. Yu, L. Wang, X. Hu, Z. Xie, Q. Liu, B. Yang, X. Wang et al. , “Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array,” Nature Communications , vol. 15, no. 1, p. 9513, 2024

2024

Later among the works it cites.

K. Frans, D. Hafner, S. Levine, and P. Abbeel, “One step diffusion via shortcut models,” arXiv preprint arXiv:2410.12557 , 2024

Original