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Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping.
Retargetting motion to new characters
M. Gleicher · 1998
Earlier work this paper cites.
Graspit! a versatile simulator for robotic grasping
A. T. Miller and P. K. Allen · 2004
Earlier work this paper cites.
Dexterous manipulation using both palm and fingers
Y. Bai and C. K. Liu · 2014
Earlier work this paper cites.
Real-time behaviour synthesis for dynamic hand-manipulation
V. Kumar, Y. Tassa, T. Erez, and E. Todorov · 2014
Earlier work this paper cites.
The YCB object and model set: Towards common benchmarks for manipulation research
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
Earlier work this paper cites.
Optimal control with learned local models: Application to dexterous manipulation
V. Kumar, E. Todorov, and S. Levine · 2016
Earlier work this paper cites.
Learning dexterous manipulation for a soft robotic hand from human demonstrations
A. Gupta, C. Eppner, S. Levine, and P. Abbeel · 2016
Earlier work this paper cites.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg · 2017
Earlier work this paper cites.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Earlier work this paper cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller · 2017
Earlier work this paper cites.
The effectiveness of data augmentation in image classification using deep learning
L. Perez and J. Wang · 2017
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.
Domain randomization and generative models for robotic grasping
J. Tobin, L. Biewald, R. Duan, M. Andrychowicz, A. Handa, V. Kumar, B. McGrew, A. Ray, J. Schneider, P. Welinder, et al · 2018
Earlier work this paper cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine · 2018
Earlier work this paper cites.
Learning dexterous in-hand manipulation
OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Józefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba · 2018
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First-person hand action benchmark with RGB-D videos and 3D hand pose annotations
G. Garcia-Hernando, S. Yuan, S. Baek, and T.-K. Kim · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
Training deep networks with synthetic data: Bridging the reality gap by domain randomization
J. Tremblay, A. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield · 2018
Cited alongside, same era.
Deep dynamics models for learning dexterous manipulation
A. Nagabandi, K. Konoglie, S. Levine, and V. Kumar · 2019
GRAB: A dataset of whole-body human grasping of objects
O. Taheri, N. Ghorbani, M. J. Black, and D. Tzionas · 2020
Later among the works it cites.
Learning invariances in neural networks from training data
G. Benton, M. Finzi, P. Izmailov, and A. G. Wilson · 2020
Later among the works it cites.
A PyTorch extension: Tools for easy mixed precision and distributed training in PyTorch
N. Corporation · 2020
Later among the works it cites.
DexVIP: Learning dexterous grasping with human hand pose priors from video
P. Mandikal and K. Grauman · 2021
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A system for general in-hand object re-orientation
T. Chen, J. Xu, and P. Agrawal · 2021
Later among the works it cites.
DexMV: Imitation learning for dexterous manipulation from human videos
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6-DOF GraspNet: Variational grasp generation for object manipulation
A. Mousavian, C. Eppner, and D. Fox · 2019
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ContactGrasp: Functional multi-finger grasp synthesis from contact
S. Brahmbhatt, A. Handa, J. Hays, and D. Fox · 2019
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Solving Rubik’s cube with a robot hand
OpenAI, I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng, Q. Yuan, W. Zaremba, and L. Zhang · 2019
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Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar · 2019
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Learning joint reconstruction of hands and manipulated objects
Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, and C. Schmid · 2019
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A survey on image data augmentation for deep learning
C. Shorten and T. M. Khoshgoftaar · 2019
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Learning dexterous manipulation from suboptimal experts
R. Jeong, J. T. Springenberg, J. Kay, D. Zheng, A. Galashov, N. Heess, and F. Nori · 2020
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Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang · 2021
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Learning dexterous grasping with object-centric visual affordances
P. Mandikal and K. Grauman · 2021
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox · 2021
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Deformed implicit field: Modeling 3d shapes with learned dense correspondence
Y. Deng, J. Yang, and X. Tong · 2021
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PyBullet: a Python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2021
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DexYCB: A benchmark for capturing hand grasping of objects
Y.-W. Chao, W. Yang, Y. Xiang, P. Molchanov, A. Handa, J. Tremblay, Y. S. Narang, K. Van Wyk, U. Iqbal, S. Birchfield, J. Kautz, and D. Fox · 2021
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State-only imitation learning for dexterous manipulation
I. Radosavovic, X. Wang, L. Pinto, and J. Malik · 2021
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Google scanned objects: A high-quality dataset of 3d scanned household items
L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V. Vanhoucke · 2022
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Distributionally robust policy learning via adversarial environment generation
A. Z. Ren and A. Majumdar · 2022
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Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior
K. Van Wyk, M. Xie, A. Li, M. A. Rana, B. Babich, B. Peele, Q. Wan, I. Akinola, B. Sundaralingam, D. Fox, B. Boots, and N. Ratliff · 2022
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