Literature
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[
1]
S. Bauer, F. Widmaier, M. Wüthrich, A. Buchholz, S. Stark, A. Goyal, T. Steinbrenner, J. Akpo, S. Joshi, V. Berenz, V. Agrawal, N. Funk, J. U. D. Jesus, J. Peters, J. Watson, C. Chen, K. Srinivasan, J. Zhang, J. Zhang, M. R. Walter, R. Madan, C. Schaff, T. Maeda, T. Yoneda, D. Yarats, A. Allshire, E. K. Gordon, T. Bhattacharjee, S. S. Srinivasa, A. Garg, H. Sikchi, J. Wang, Q. Yao, S. Yang, R. McCarthy, F. R. Sanchez, Q. Wang, D. C. Bulens, K. McGuinness, N. O'Connor, S. J. Redmond, and B. Schölkopf,
Real robot challenge: A robotics competition in the cloud, 2021. [
arXiv ]
[
2]
M. Wuthrich, F. Widmaier, F. Grimminger, S. Joshi, V. Agrawal, B. Hammoud, M. Khadiv, M. Bogdanovic, V. Berenz, J. Viereck, M. Naveau, L. Righetti, B. Schölkopf, and S. Bauer,
Trifinger: An open-source robot for learning dexterity, in 4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA, J. Kober, F. Ramos, and C. J. Tomlin, eds., vol. 155 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 1871--1882. [
DOI |
arXiv ]
[
3]
D. Agudelo-España, A. Zadaianchuk, P. Wenk, A. Garg, J. Akpo, F. Grimminger, J. Viereck, M. Naveau, L. Righetti, G. Martius, A. Krause, B. Schölkopf, S. Bauer, and M. Wüthrich,
A real-robot dataset for assessing transferability of learned dynamics models, in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020, IEEE, 2020, pp. 8151--8157. [
DOI ]
[
4]
O. Ahmed, F. Träuble, A. Goyal, A. Neitz, M. Wuthrich, Y. Bengio, B. Schölkopf, and S. Bauer,
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning, in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, 2021. [
DOI |
arXiv ]
[
5]
N. Funk, C. B. Schaff, R. Madan, T. Yoneda, J. U. D. Jesus, J. Watson, E. K. Gordon, F. Widmaier, S. Bauer, S. S. Srinivasa, T. Bhattacharjee, M. R. Walter, and J. Peters,
Benchmarking structured policies and policy optimization for real-world dexterous object manipulation, IEEE Robotics Autom. Lett., 7 (2022), pp. 478--485. [
DOI ]
[
6]
Q. Wang, F. R. Sanchez, R. McCarthy, D. C. Bulens, K. McGuinness, N. E. O'Connor, M. Wüthrich, F. Widmaier, S. Bauer, and S. J. Redmond,
Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward-based tasks, CoRR, abs/2205.09683 (2022). [
arXiv ]
[
7]
A. Allshire, M. Mittal, V. Lodaya, V. Makoviychuk, D. Makoviichuk, F. Widmaier, M. Wüthrich, S. Bauer, A. Handa, and A. Garg,
Transferring dexterous manipulation from GPU simulation to a remote real-world trifinger, CoRR, abs/2108.09779 (2021). [
arXiv ]
[
8]
R. F. Prudencio, M. R. O. A. Maximo, and E. L. Colombini,
A survey on offline reinforcement learning: Taxonomy, review, and open problems , CoRR, abs/2203.01387 (2020). [
arXiv ]
[
9]
S. Levine, A. Kumar, G. Tucker, and J. Fu,
Offline reinforcement learning: Tutorial, review, and perspectives on open problems , CoRR, abs/2005.01643 (2020). [
arXiv ]
[
10]
T. Seno and M. Imai,
d3rlpy: An offline deep reinforcement learning library , CoRR, abs/2111.03788 (2021). [
arXiv ]
[
11]
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine,
D4RL: datasets for deep data-driven reinforcement learning , CoRR, abs/2004.07219 (2020). [
arXiv ]
