Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards

G. E. Uhlenbeck and L. S. Ornstein, “On the theory of the brownian motion,”

1930

Earlier work this paper cites.

R. Hartley and A. Zisserman,

2002

Earlier work this paper cites.

T. Lillicrap, “Continuous control with deep reinforcement learning,”

Original

2015

Earlier work this paper cites.

V. Kumar, E. Todorov, and S. Levine, “Optimal control with learned local models: Application to dexterous manipulation,”

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in

2016

Earlier work this paper cites.

L. Keselman, J. Iselin Woodfill, A. Grunnet-Jepsen, and A. Bhowmik, “Intel RealSense Stereoscopic Depth Cameras,”

Original

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

2017

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,”

Original

2019

Earlier work this paper cites.

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, “Solving Rubik’s Cube with a Robot Hand,”

Original

2019

Earlier work this paper cites.

T. Yenamandra, F. Bernard, J. Wang, F. Mueller, and C. Theobalt, “Convex Optimisation for Inverse Kinematics,”

Original

2019

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

2020

Earlier work this paper cites.

J. Pari, N. M. Shafiullah, S. P. Arunachalam, and L. Pinto, “The surprising effectiveness of representation learning for visual imitation,” 2021

2021

Earlier work this paper cites.

M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging Properties in Self-Supervised Vision Transformers,”

Original

2021

Earlier work this paper cites.

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto, “Mastering visual continuous control: Improved data-augmented reinforcement learning,”

Original

2021

Earlier work this paper cites.

Z. J. Cui, Y. Wang, N. M. M. Shafiullah, and L. Pinto, “From play to policy: Conditional behavior generation from uncurated robot data,”

Original

2022

Earlier work this paper cites.

S. Pandian Arunachalam, I. Güzey, S. Chintala, and L. Pinto, “Holo-Dex: Teaching Dexterity with Immersive Mixed Reality,”

Original

2022

Earlier work this paper cites.

S. Kumar, J. Zamora, N. Hansen, R. Jangir, and X. Wang, “Graph inverse reinforcement learning from diverse videos,”

Original

2022

Earlier work this paper cites.

A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam, Y. Narang, J.-F. Lafleche, D. Fox, and G. State, “DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality,”

Original

2022

Cited alongside, same era.

S. P. Arunachalam, I. Güzey, S. Chintala, and L. Pinto, “Holo-dex: Teaching dexterity with immersive mixed reality,”

Original

2022

Cited alongside, same era.

S. Haldar, V. Mathur, D. Yarats, and L. Pinto, “Watch and match: Supercharging imitation with regularized optimal transport,”

Original

2022

Cited alongside, same era.

K. Pertsch, R. Desai, V. Kumar, F. Meier, J. J. Lim, D. Batra, and A. Rai, “Cross-Domain Transfer via Semantic Skill Imitation,”

Original

2022

Cited alongside, same era.

S. Haldar, V. Mathur, D. Yarats, and L. Pinto, “Watch and match: Supercharging imitation with regularized optimal transport,” in

2023

Later among the works it cites.

A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment Anything,”

Original

2023

Later among the works it cites.

N. Karaev, I. Rocco, B. Graham, N. Neverova, A. Vedaldi, and C. Rupprecht, “Cotracker: It is better to track together,”

Original

2023

Later among the works it cites.

S. Lee, Y. Wang, H. Etukuru, H. J. Kim, N. M. Mahi Shafiullah, and L. Pinto, “Behavior Generation with Latent Actions,”

Original

2024

Closest in time.

C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,”