Built on

D. Lee and M. W. Spong, “Passive bilateral teleoperation with constant time delay,” IEEE transactions on robotics , vol. 22, no. 2, pp. 269–281, 2006
2006
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” arXiv preprint arXiv:1511.05644 , 2015
Original
2015
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems , C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., vol. 28. Curran Associates, Inc., 2015
2015
Earlier work this paper cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework,” ICRL (Poster) , 2016
2016
Earlier work this paper cites.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” Advances in neural information processing systems , vol. 29, pp. 4743–4751, 2016
2016
Earlier work this paper cites.
G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 1433–1440
2016
Earlier work this paper cites.
I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf, “Wasserstein auto-encoders,” arXiv preprint arXiv:1711.01558 , 2017
Original
2017
Earlier work this paper cites.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba, “Hindsight experience replay,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
M. T. Mason, “Toward robotic manipulation,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 1, pp. 1–28, 2018
2018
Earlier work this paper cites.
F. Ruggiero, V. Lippiello, and B. Siciliano, “Nonprehensile dynamic manipulation: A survey,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 1711–1718, 2018
2018
Earlier work this paper cites.

Similar

A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Cited alongside, same era.
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 preprint arXiv:1910.07113 , 2019
Original
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,” arXiv preprint arXiv:1910.11956 , 2019
Original
2019
Cited alongside, same era.

Then

C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet, “Learning latent plans from play,” in Conference on Robot Learning , 2020, pp. 1113–1132
2020
Later among the works it cites.
A. Ajay, A. Kumar, P. Agrawal, S. Levine, and O. Nachum, “Opal: Offline primitive discovery for accelerating offline reinforcement learning,” arXiv preprint arXiv:2010.13611 , 2020
Original
2020
Later among the works it cites.
B. Ichter, P. Sermanet, and C. Lynch, “Broadly-exploring, local-policy trees for long-horizon task planning,” arXiv preprint arXiv:2010.06491 , 2020
Original
2020
Later among the works it cites.
K. Pertsch, Y. Lee, and J. J. Lim, “Accelerating reinforcement learning with learned skill priors,” arXiv preprint arXiv:2010.11944 , 2020
Original
2020
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2022

Learning Dynamic Manipulation Skills from Haptic-Play

Lee, Taeyoon, Sung, Donghyun, Choi, Kyoungyeon et al.

Understand

In this paper, we propose a data-driven skill learning approach to solve highly dynamic manipulation tasks entirely from offline teleoperated play data.

We use a bilateral teleoperation system to continuously collect a large set of dexterous and agile manipulation behaviors, which is enabled by providing direct force feedback to the operator.
We jointly learn the state conditional latent skill distribution and skill decoder network in the form of goal-conditioned policy and skill conditional state transition dynamics using a two-stage generative modeling framework.
This allows one to perform robust model-based planning, both online and offline planning methods, in the learned skill-space to accomplish any given downstream tasks at test time.

Built on

D. Lee and M. W. Spong, “Passive bilateral teleoperation with constant time delay,” IEEE transactions on robotics , vol. 22, no. 2, pp. 269–281, 2006
2006
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” arXiv preprint arXiv:1511.05644 , 2015
Original
2015
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems , C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., vol. 28. Curran Associates, Inc., 2015
2015
Earlier work this paper cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework,” ICRL (Poster) , 2016
2016
Earlier work this paper cites.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved variational inference with inverse autoregressive flow,” Advances in neural information processing systems , vol. 29, pp. 4743–4751, 2016
2016
Earlier work this paper cites.
G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 1433–1440
2016
Earlier work this paper cites.
I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf, “Wasserstein auto-encoders,” arXiv preprint arXiv:1711.01558 , 2017
Original
2017
Earlier work this paper cites.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba, “Hindsight experience replay,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
M. T. Mason, “Toward robotic manipulation,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 1, pp. 1–28, 2018
2018
Earlier work this paper cites.
F. Ruggiero, V. Lippiello, and B. Siciliano, “Nonprehensile dynamic manipulation: A survey,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 1711–1718, 2018
2018
Earlier work this paper cites.

Similar

A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Cited alongside, same era.
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 preprint arXiv:1910.07113 , 2019
Original
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,” arXiv preprint arXiv:1910.11956 , 2019
Original
2019
Cited alongside, same era.

Then

C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet, “Learning latent plans from play,” in Conference on Robot Learning , 2020, pp. 1113–1132
2020
Later among the works it cites.
A. Ajay, A. Kumar, P. Agrawal, S. Levine, and O. Nachum, “Opal: Offline primitive discovery for accelerating offline reinforcement learning,” arXiv preprint arXiv:2010.13611 , 2020
Original
2020
Later among the works it cites.
B. Ichter, P. Sermanet, and C. Lynch, “Broadly-exploring, local-policy trees for long-horizon task planning,” arXiv preprint arXiv:2010.06491 , 2020
Original
2020
Later among the works it cites.
K. Pertsch, Y. Lee, and J. J. Lim, “Accelerating reinforcement learning with learned skill priors,” arXiv preprint arXiv:2010.11944 , 2020
Original
2020
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

K. Fang, Y. Zhu, A. Garg, S. Savarese, and L. Fei-Fei, “Dynamics learning with cascaded variational inference for multi-step manipulation,” arXiv preprint arXiv:1910.13395 , 2019

Original

2019

Cited alongside, same era.

A. Sharma, S. Gu, S. Levine, V. Kumar, and K. Hausman, “Dynamics-aware unsupervised discovery of skills,” arXiv preprint arXiv:1907.01657 , 2019

Original

2019

Cited alongside, same era.

X. B. Peng, M. Chang, G. Zhang, P. Abbeel, and S. Levine, “Mcp: Learning composable hierarchical control with multiplicative compositional policies,” arXiv preprint arXiv:1905.09808 , 2019

Original

2019

Cited alongside, same era.

B. Katz, J. Di Carlo, and S. Kim, “Mini cheetah: A platform for pushing the limits of dynamic quadruped control,” in 2019 international conference on robotics and automation (ICRA) . IEEE, 2019, pp. 6295–6301

2019

Cited alongside, same era.

S. Zhao, J. Song, and S. Ermon, “Infovae: Balancing learning and inference in variational autoencoders,” in Proceedings of the aaai conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 5885–5892

2019

Cited alongside, same era.

B. Dai and D. Wipf, “Diagnosing and enhancing vae models,” arXiv preprint arXiv:1903.05789 , 2019

Original

2019

Cited alongside, same era.

S. Levine, A. Kumar, G. Tucker, and J. Fu, “Offline reinforcement learning: Tutorial, review, and perspectives on open problems,” arXiv preprint arXiv:2005.01643 , 2020

Original

2020