Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand

OpenAI et al. , “Solving rubik’s cube with a robot hand,” CoRR , vol. abs/1910.07113, 2019. [Online]. Available: http://arxiv.org/abs/1910.07113

Original

1910

Earlier work this paper cites.

R. Fearing, “Implementing a force strategy for object re-orientation,” in Proceedings. 1986 IEEE International Conference on Robotics and Automation , vol. 3, 1986, pp. 96–102

1986

Earlier work this paper cites.

J. Butterfaß, M. Grebenstein, H. Liu, and G. Hirzinger, “DLR-Hand II: Next generation of a dextrous robot hand,” in Proc. IEEE International Conference on Robotics and Automation , 2001, pp. 109–114

2001

Earlier work this paper cites.

I. Mordatch, Z. Popovic, and E. Todorov, “Contact-invariant optimization for hand manipulation,” in Proceedings of the 2012 Eurographics/ACM SIGGRAPH Symposium on Computer Animation , 2012

2012

Earlier work this paper cites.

Y. Bai and C. K. Liu, “Dexterous manipulation using both palm and fingers,” in Proc IEEE International Conference on Robotics and Automation , 2014

2014

Earlier work this paper cites.

B. Bäuml et al. , “Agile justin: An upgraded member of DLR’s family of lightweight and torque controlled humanoids,” in Proc. IEEE International Conference on Robotics and Automation , 2014

2014

Earlier work this paper cites.

M. Li, H. Yin, K. Tahara, and A. Billard, “Learning object-level impedance control for robust grasping and dexterous manipulation,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) , 2014, pp. 6784–6791

2014

Earlier work this paper cites.

H. van Hoof, T. Hermans, G. Neumann, and J. Peters, “Learning robot in-hand manipulation with tactile features,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , 2015, pp. 121–127

2015

Earlier work this paper cites.

V. Kumar, E. Todorov, and S. Levine, “Optimal control with learned local models: Application to dexterous manipulation,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 378–383

2016

Cited alongside, same era.

A. Rajeswaran et al. , “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” CoRR , vol. abs/1709.10087, 2017. [Online]. Available: http://arxiv.org/abs/1709.10087

Original

2017

Cited alongside, same era.

J. Schulman et al. , “Proximal policy optimization algorithms,” CoRR , vol. abs/1707.06347, 2017. [Online]. Available: http://arxiv.org/abs/1707.06347

Original

2017

Cited alongside, same era.

OpenAI et al. , “Learning dexterous in-hand manipulation,” CoRR , 2018. [Online]. Available: http://arxiv.org/abs/1808.00177

Original

2018

Cited alongside, same era.

M. C. Machado et al. , “Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents,” J. Artif. Intell. Res. , vol. 61, pp. 523–562, 2018

2018

Later among the works it cites.

M. Plappert et al. , “Multi-goal reinforcement learning: Challenging robotics environments and request for research,” CoRR , vol. abs/1802.09464, 2018

Original

2018

Later among the works it cites.

T. Li et al. , “Learning hierarchical control for robust in-hand manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 8855–8862

2020

Later among the works it cites.

A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Proceedings of the Conference on Robot Learning , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 30 Oct–01 Nov 2020, pp. 1101–1112

2020

Later among the works it cites.