Fetching the paper…
Reading the bibliography…
In this work, we aim to learn a unified vision-based policy for multi-fingered robot hands to manipulate a variety of objects in diverse poses.
L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: A survey,” Journal of artificial intelligence research , 1996
1996
Earlier work this paper cites.
L. Han and J. C. Trinkle, “Dextrous manipulation by rolling and finger gaiting,” in ICRA , 1998
1998
Earlier work this paper cites.
D. Rus, “In-hand dexterous manipulation of piecewise-smooth 3D objects,” IJRR , 1999
1999
Earlier work this paper cites.
R. Rubinstein, “The cross-entropy method for combinatorial and continuous optimization,” Methodology and computing in applied probability , 1999
1999
Earlier work this paper cites.
A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in ICRA , 2000
2000
Earlier work this paper cites.
I. Mordatch, Z. Popović, and E. Todorov, “Contact-invariant optimization for hand manipulation,” in SIGGRAPH , 2012
2012
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
Earlier work this paper cites.
J. Schulman et al. , “Trust region policy optimization,” in ICML , 2015
2015
Earlier work this paper cites.
J. Merel, Y. Tassa, D. TB, S. Srinivasan, J. Lemmon, Z. Wang, G. Wayne, and N. Heess, “Learning human behaviors from motion capture by adversarial imitation,” arXiv , 2017
2017
Earlier work this paper cites.
J. Romero, D. Tzionas, and M. J. Black, “Embodied Hands: Modeling and capturing hands and bodies together,” TOG , 2017
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv , 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3D classification and segmentation,” in CVPR , 2017
2017
Earlier work this paper cites.
M. T. Mason, “Toward robotic manipulation,” Annual Review of Control, Robotics, and Autonomous Systems , 2018
2018
Earlier work this paper cites.
R. S. Sutton, “Reinforcement learning: An introduction,” A Bradford Book , 2018
2018
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,” in RSS , 2018
2018
Earlier work this paper cites.
D. Antotsiou, G. Garcia-Hernando, and T.-K. Kim, “Task-oriented hand motion retargeting for dexterous manipulation imitation,” in ECCV , 2018
2018
Earlier work this paper cites.
B. Kang, Z. Jie, and J. Feng, “Policy optimization with demonstrations,” in ICML , 2018
2018
Earlier work this paper cites.
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas et al. , “Solving rubik’s cube with a robot hand,” arXiv , 2019
2019
Earlier work this paper cites.
Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, and C. Schmid, “Learning joint reconstruction of hands and manipulated objects,” in CVPR , 2019
2019
Earlier work this paper cites.
G. J. Steven, “The nlopt nonlinear-optimization package,” 2019
2019
Earlier work this paper cites.
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “RLBench: The robot learning benchmark & learning environment,” RAL , 2020
2020
Earlier work this paper cites.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in CoRL , 2020
2020
Earlier work this paper cites.
L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine, “AVID: Learning multi-stage tasks via pixel-level translation of human videos,” in RSS , 2020
2020
Earlier work this paper cites.
K. Schmeckpeper, O. Rybkin, K. Daniilidis, S. Levine, and C. Finn, “Reinforcement learning with videos: Combining offline observations with interaction,” in CoRL , 2020
2020
Earlier work this paper cites.
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg, “Concept2Robot: Learning manipulation concepts from instructions and human demonstrations,” in RSS , 2020
2020
Earlier work this paper cites.
A. Handa, K. Van Wyk, W. Yang, J. Liang, Y.-W. Chao, Q. Wan, S. Birchfield, N. Ratliff, and D. Fox, “DexPilot: Vision-based teleoperation of dexterous robotic hand-arm system,” in ICRA , 2020
2020
Earlier work this paper cites.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas et al. , “SAPIEN: A simulated part-based interactive environment,” in CVPR , 2020
2020
Earlier work this paper cites.
T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” in CoRL , 2021
2021
Earlier work this paper cites.
H. Jiang, S. Liu, J. Wang, and X. Wang, “Hand-object contact consistency reasoning for human grasps generation,” in ICCV , 2021
2021
Cited alongside, same era.
Y.-W. Chao, W. Yang, Y. Xiang, P. Molchanov, A. Handa, J. Tremblay, Y. S. Narang, K. Van Wyk, U. Iqbal, S. Birchfield et al. , “DexYCB: A benchmark for capturing hand grasping of objects,” in CVPR , 2021
2021
Cited alongside, same era.
K. Zakka, A. Zeng, P. Florence, J. Tompson, J. Bohg, and D. Dwibedi, “XIRL: Cross-embodiment inverse reinforcement learning,” in CoRL , 2021
2021
Cited alongside, same era.
A. S. Chen, S. Nair, and C. Finn, “Learning generalizable robotic reward functions from” in-the-wild” human videos,” in RSS , 2021
2021
Cited alongside, same era.
H. Xiong, Q. Li, Y.-C. Chen, H. Bharadhwaj, S. Sinha, and A. Garg, “Learning by watching: Physical imitation of manipulation skills from human videos,” in IROS , 2021
H. Qi, B. Yi, S. Suresh, M. Lambeta, Y. Ma, R. Calandra, and J. Malik, “General in-hand object rotation with vision and touch,” in CoRL , 2023
2023
Later among the works it cites.
K. Zakka, P. Wu, L. Smith, N. Gileadi, T. Howell, X. B. Peng, S. Singh, Y. Tassa, P. Florence, A. Zeng et al. , “RoboPianist: Dexterous piano playing with deep reinforcement learning,” in CoRL , 2023
2023
Later among the works it cites.
Y. Qin, W. Yang, B. Huang, K. Van Wyk, H. Su, X. Wang, Y.-W. Chao, and D. Fox, “AnyTeleop: A general vision-based dexterous robot arm-hand teleoperation system,” in RSS , 2023
2023
Later among the works it cites.
S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto, “Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation,” in ICRA , 2023
2023
Later among the works it cites.
S. P. Arunachalam, I. Güzey, S. Chintala, and L. Pinto, “Holo-Dex: Teaching dexterity with immersive mixed reality,” in ICRA , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
P. Mandikal and K. Grauman, “Learning dexterous grasping with object-centric visual affordances,” in ICRA , 2021
2021
Cited alongside, same era.
I. Radosavovic, X. Wang, L. Pinto, and J. Malik, “State-only imitation learning for dexterous manipulation,” in IROS , 2021
2021
Cited alongside, same era.
H. Qi, A. Kumar, R. Calandra, Y. Ma, and J. Malik, “In-hand object rotation via rapid motor adaptation,” in CoRL , 2022
2022
Cited alongside, same era.
H. Xu, Y. Luo, S. Wang, T. Darrell, and R. Calandra, “Towards learning to play piano with dexterous hands and touch,” in IROS , 2022
2022
Cited alongside, same era.
L. Yang, K. Li, X. Zhan, F. Wu, A. Xu, L. Liu, and C. Lu, “OakInk: A large-scale knowledge repository for understanding hand-object interaction,” in CVPR , 2022
2022
Cited alongside, same era.
Y. Liu, Y. Liu, C. Jiang, K. Lyu, W. Wan, H. Shen, B. Liang, Z. Fu, H. Wang, and L. Yi, “HOI4D: A 4D egocentric dataset for category-level human-object interaction,” in CVPR , 2022
2022
Cited alongside, same era.
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 ECCV , 2022
2022
Cited alongside, same era.
2023
Later among the works it cites.
Q. Liu, Y. Cui, Q. Ye, Z. Sun, H. Li, G. Li, L. Shao, and J. Chen, “DexRepNet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations,” in IROS , 2023
2023
Later among the works it cites.
S. Dasari, A. Gupta, and V. Kumar, “Learning dexterous manipulation from exemplar object trajectories and pre-grasps,” in ICRA , 2023
2023
Later among the works it cites.
X. Zhu, J. Ke, Z. Xu, Z. Sun, B. Bai, J. Lv, Q. Liu, Y. Zeng, Q. Ye, C. Lu et al. , “Diff-LfD: Contact-aware model-based learning from visual demonstration for robotic manipulation via differentiable physics-based simulation and rendering,” in CoRL , 2023
2023
Later among the works it cites.
Y. Xu, W. Wan, J. Zhang, H. Liu, Z. Shan, H. Shen, R. Wang, H. Geng, Y. Weng, J. Chen et al. , “UniDexGrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,” in CVPR , 2023
2023
Later among the works it cites.
C. Bao, H. Xu, Y. Qin, and X. Wang, “DexArt: Benchmarking generalizable dexterous manipulation with articulated objects,” in CVPR , 2023
2023
Later among the works it cites.
Y. Han, M. Xie, Y. Zhao, and H. Ravichandar, “On the utility of koopman operator theory in learning dexterous manipulation skills,” in CoRL , 2023
2023
Later among the works it cites.
T. Chen, M. Tippur, S. Wu, V. Kumar, E. Adelson, and P. Agrawal, “Visual dexterity: In-hand reorientation of novel and complex object shapes,” Science Robotics , 2023
2023
Later among the works it cites.
W. Wan, H. Geng, Y. Liu, Z. Shan, Y. Yang, L. Yi, and H. Wang, “UniDexGrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning,” in ICCV , 2023
2023
Later among the works it cites.
N. Hansen, Y. Lin, H. Su et al. , “MoDem: Accelerating visual model-based reinforcement learning with demonstrations,” in ICLR , 2023
2023
Later among the works it cites.
Z. Chen, S. Chen, C. Schmid, and I. Laptev, “gSDF: Geometry-Driven signed distance functions for 3D hand-object reconstruction,” in CVPR , 2023
2023
Later among the works it cites.
I. Radosavovic, T. Xiao, S. James, P. Abbeel, J. Malik, and T. Darrell, “Real-world robot learning with masked visual pre-training,” in CoRL , 2023
2023
Later among the works it cites.
Y. Ze, Y. Liu, R. Shi, J. Qin, Z. Yuan, J. Wang, and H. Xu, “H-InDex:visual reinforcement learning with hand-informed representations for dexterous manipulation,” in NeurIPS , 2023
2023
Later among the works it cites.
E. Chane-Sane, C. Schmid, and I. Laptev, “Learning video-conditioned policies for unseen manipulation tasks,” in ICRA , 2023
2023
Later among the works it cites.
M. Alakuijala, G. Dulac-Arnold, J. Mairal, J. Ponce, and C. Schmid, “Learning reward functions for robotic manipulation by observing humans,” in ICRA , 2023
2023
Later among the works it cites.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in RSS , 2023
2023
Later among the works it cites.
C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu, “DexCap: Scalable and portable mocap data collection system for dexterous manipulation,” in RSS , 2024
2024
Closest in time.
S. Yang, M. Liu, Y. Qin, D. Runyu, L. Jialong, X. Cheng, R. Yang, S. Yi, and X. Wang, “ACE: A cross-platfrom visual-exoskeletons for low-cost dexterous teleoperation,” in CoRL , 2024
2024
Closest in time.
W. Yang and W. Jin, “ContactSDF: Signed distance functions as multi-contact models for dexterous manipulation,” arXiv , 2024
2024
Closest in time.
T. Lin, Y. Zhang, Q. Li, H. Qi, B. Yi, S. Levine, and J. Malik, “Learning visuotactile skills with two multifingered hands,” arXiv , 2024
2024
Closest in time.
J. Wang, Y. Qin, K. Kuang, Y. Korkmaz, A. Gurumoorthy, H. Su, and X. Wang, “CyberDemo: Augmenting simulated human demonstration for real-world dexterous manipulation,” in CVPR , 2024
2024
Closest in time.
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 ICRA , 2024
2024
Closest in time.
Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3D diffusion policy: Generalizable visuomotor policy learning via simple 3D representations,” in RSS , 2024
2024
Closest in time.