Built on

A. Quattoni and A. Torralba, “Recognizing indoor scenes,” in 2009 IEEE conference on computer vision and pattern recognition . IEEE, 2009, pp. 413–420
2009
Earlier work this paper cites.
S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , ser. Proceedings of Machine Learning Research, Y. W. Teh and M. Titterington, Eds., vol. 9. Chia Laguna Resort, Sardinia, Italy: PMLR, 13–15 May 2010, pp. 661–668. [Online]. Available: https://proceedings.mlr.press/v9/ross10a.html
2010
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.
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.
J. A. Häusser, S. Schulz-Hardt, T. Schultze, A. Tomaschek, and A. Mojzisch, “Experimental evidence for the effects of task repetitiveness on mental strain and objective work performance,” Journal of Organizational Behavior , vol. 35, no. 5, pp. 705–721, 2014
2014
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 3606–3613
2014
Earlier work this paper cites.
M. Daneshmand, A. Helmi, E. Avots, F. Noroozi, F. Alisinanoglu, H. S. Arslan, J. Gorbova, R. E. Haamer, C. Ozcinar, and G. Anbarjafari, “3d scanning: A comprehensive survey,” arXiv preprint arXiv:1801.08863 , 2018
Original
2018
Earlier work this paper cites.
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” International journal of computer vision , vol. 128, no. 7, pp. 1956–1981, 2020
2020
Earlier work this paper cites.
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine, “Bridge data: Boosting generalization of robotic skills with cross-domain datasets,” arXiv preprint arXiv:2109.13396 , 2021
Original
2021
Earlier work this paper cites.

Similar

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,” 2021
2021
Cited alongside, same era.
M. Mosbach, K. Moraw, and S. Behnke, “Accelerating interactive human-like manipulation learning with gpu-based simulation and high-quality demonstrations,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 435–441
2022
Cited alongside, same era.
K. Zakka, Y. Tassa, and MuJoCo Menagerie Contributors, “MuJoCo Menagerie: A collection of high-quality simulation models for MuJoCo,” 2022. [Online]. Available: http://github.com/google-deepmind/mujoco_menagerie
2022
Cited alongside, same era.
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,” arXiv preprint arXiv:2307.04577 , 2023
Original
2023

Then

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2024

DexHub and DART: Towards Internet Scale Robot Data Collection

Park, Younghyo, Bhatia, Jagdeep Singh, Ankile, Lars et al.

Understand

The quest to build a generalist robotic system is impeded by the scarcity of diverse and high-quality data.

While real-world data collection effort exist, requirements for robot hardware, physical environment setups, and frequent resets significantly impede the scalability needed for modern learning frameworks.
We introduce DART, a teleoperation platform designed for crowdsourcing that reimagines robotic data collection by leveraging cloud-based simulation and augmented reality (AR) to address many limitations of prior data collection efforts.
Our user studies highlight that DART enables higher data collection throughput and lower physical fatigue compared to real-world teleoperation.

Built on

A. Quattoni and A. Torralba, “Recognizing indoor scenes,” in 2009 IEEE conference on computer vision and pattern recognition . IEEE, 2009, pp. 413–420
2009
Earlier work this paper cites.
S. Ross and D. Bagnell, “Efficient reductions for imitation learning,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , ser. Proceedings of Machine Learning Research, Y. W. Teh and M. Titterington, Eds., vol. 9. Chia Laguna Resort, Sardinia, Italy: PMLR, 13–15 May 2010, pp. 661–668. [Online]. Available: https://proceedings.mlr.press/v9/ross10a.html
2010
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.
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.
J. A. Häusser, S. Schulz-Hardt, T. Schultze, A. Tomaschek, and A. Mojzisch, “Experimental evidence for the effects of task repetitiveness on mental strain and objective work performance,” Journal of Organizational Behavior , vol. 35, no. 5, pp. 705–721, 2014
2014
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 3606–3613
2014
Earlier work this paper cites.
M. Daneshmand, A. Helmi, E. Avots, F. Noroozi, F. Alisinanoglu, H. S. Arslan, J. Gorbova, R. E. Haamer, C. Ozcinar, and G. Anbarjafari, “3d scanning: A comprehensive survey,” arXiv preprint arXiv:1801.08863 , 2018
Original
2018
Earlier work this paper cites.
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” International journal of computer vision , vol. 128, no. 7, pp. 1956–1981, 2020
2020
Earlier work this paper cites.
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine, “Bridge data: Boosting generalization of robotic skills with cross-domain datasets,” arXiv preprint arXiv:2109.13396 , 2021
Original
2021
Earlier work this paper cites.

Similar

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,” 2021
2021
Cited alongside, same era.
M. Mosbach, K. Moraw, and S. Behnke, “Accelerating interactive human-like manipulation learning with gpu-based simulation and high-quality demonstrations,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) . IEEE, 2022, pp. 435–441
2022
Cited alongside, same era.
K. Zakka, Y. Tassa, and MuJoCo Menagerie Contributors, “MuJoCo Menagerie: A collection of high-quality simulation models for MuJoCo,” 2022. [Online]. Available: http://github.com/google-deepmind/mujoco_menagerie
2022
Cited alongside, same era.
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,” arXiv preprint arXiv:2307.04577 , 2023
Original
2023

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

J. Duan, Y. R. Wang, M. Shridhar, D. Fox, and R. Krishna, “Ar2-d2: Training a robot without a robot,” arXiv preprint arXiv:2306.13818 , 2023

Original

2023

Cited alongside, same era.

T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” arXiv preprint arXiv:2304.13705 , 2023

Original

2023

Cited alongside, same era.

S. Hampali, T. Hodan, L. Tran, L. Ma, C. Keskin, and V. Lepetit, “In-hand 3d object scanning from an rgb sequence,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 079–17 088

2023

Cited alongside, same era.

A. O’Neill, A. Rehman, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, A. Jain et al. , “Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6892–6903

2024

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H.-S. Fang, H. Fang, Z. Tang, J. Liu, C. Wang, J. Wang, H. Zhu, and C. Lu, “Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 653–660

2024

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T. Z. Zhao, J. Tompson, D. Driess, P. Florence, S. K. S. Ghasemipour, C. Finn, and A. Wahid, “Aloha unleashed: A simple recipe for robot dexterity,” in 8th Annual Conference on Robot Learning

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[Online]. Available: https://support.apple.com/en-us/118507#:~:text=Apple%20Vision%20Pro%20features%20an,feel%20contact%20on%20your%20nose

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S. Chen, C. Wang, K. Nguyen, L. Fei-Fei, and C. K. Liu, “Arcap: Collecting high-quality human demonstrations for robot learning with augmented reality feedback,” arXiv preprint arXiv:2410.08464 , 2024

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J. van Haastregt, M. C. Welle, Y. Zhang, and D. Kragic, “Puppeteer your robot: Augmented reality leader-follower teleoperation,” arXiv preprint arXiv:2407.11741 , 2024

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S. Caron, Y. De Mont-Marin, R. Budhiraja, S. H. Bang, I. Domrachev, and S. Nedelchev, “Pink: Python inverse kinematics based on Pinocchio,” 2024. [Online]. Available: https://github.com/stephane-caron/pink

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