Fetching the paper…
Reading the bibliography…
In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots' motion.
M. Menéndez, J. Pardo, L. Pardo, and M. Pardo, “The jensen-shannon divergence,” Journal of the Franklin Institute , vol. 334, no. 2, pp. 307–318, 1997
1997
Earlier work this paper cites.
P. Kormushev, S. Calinon, and D. G. Caldwell, “Reinforcement learning in robotics: Applications and real-world challenges,” Robotics , vol. 2, no. 3, pp. 122–148, 2013
2013
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
A. S. Polydoros and L. Nalpantidis, “Survey of model-based reinforcement learning: Applications on robotics,” Journal of Intelligent & Robotic Systems , vol. 86, no. 2, pp. 153–173, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. R. Mahmood, D. Korenkevych, G. Vasan, W. Ma, and J. Bergstra, “Benchmarking reinforcement learning algorithms on real-world robots,” in Conference on robot learning . PMLR, 2018, pp. 561–591
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Ma, H. Xu, J. Jiang, X. Mei, and X.-P. Zhang, “Ddcgan: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion,” IEEE Transactions on Image Processing , vol. 29, pp. 4980–4995, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
J. Hua, L. Zeng, G. Li, and Z. Ju, “Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning,” Sensors , vol. 21, no. 4, p. 1278, 2021
2021
Earlier work this paper cites.
E. Johns, “Coarse-to-fine imitation learning: Robot manipulation from a single demonstration,” in 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 2021, pp. 4613–4619
2021
Earlier work this paper cites.
X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: Adversarial motion priors for stylized physics-based character control,” ACM Transactions on Graphics (ToG) , vol. 40, no. 4, pp. 1–20, 2021
2021
Cited alongside, same era.
S. Pateria, B. Subagdja, A.-h. Tan, and C. Quek, “Hierarchical reinforcement learning: A comprehensive survey,” ACM Computing Surveys (CSUR) , vol. 54, no. 5, pp. 1–35, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. B. Peng, Y. Guo, L. Halper, S. Levine, and S. Fidler, “Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters,” ACM Transactions On Graphics (TOG) , vol. 41, no. 4, pp. 1–17, 2022
2022
Cited alongside, same era.
Y. Ji, Z. Li, Y. Sun, X. B. Peng, S. Levine, G. Berseth, and K. Sreenath, “Hierarchical reinforcement learning for precise soccer shooting skills using a quadrupedal robot,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1479–1486
2022
Later among the works it cites.
S. Huang, Z. Wang, J. Zhou, and J. Lu, “Planning irregular object packing via hierarchical reinforcement learning,” IEEE Robotics and Automation Letters , vol. 8, no. 1, pp. 81–88, 2022
2022
Later among the works it cites.
M. Eppe, C. Gumbsch, M. Kerzel, P. D. Nguyen, M. V. Butz, and S. Wermter, “Intelligent problem-solving as integrated hierarchical reinforcement learning,” Nature Machine Intelligence , vol. 4, no. 1, pp. 11–20, 2022
2022
Later among the works it cites.
J. Juravsky, Y. Guo, S. Fidler, and X. B. Peng, “Padl: Language-directed physics-based character control,” in SIGGRAPH Asia 2022 Conference Papers , 2022, pp. 1–9
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning . PMLR, 2022, pp. 9118–9147
2022
Cited alongside, same era.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7327–7334, 2022
2022
Cited alongside, same era.
O. Mees, L. Hermann, and W. Burgard, “What matters in language conditioned robotic imitation learning over unstructured data,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 11 205–11 212, 2022
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 894–906
2022
Cited alongside, same era.
2022
Cited alongside, same era.
A. Hu, G. Corrado, N. Griffiths, Z. Murez, C. Gurau, H. Yeo, A. Kendall, R. Cipolla, and J. Shotton, “Model-based imitation learning for urban driving,” Advances in Neural Information Processing Systems , vol. 35, pp. 20 703–20 716, 2022
2022
Cited alongside, same era.
2023
Closest in time.
N. Di Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller, “Towards a unified agent with foundation models,” in Workshop on Reincarnating Reinforcement Learning at ICLR 2023 , 2023
2023
Closest in time.
O. Mees, J. Borja-Diaz, and W. Burgard, “Grounding language with visual affordances over unstructured data,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 576–11 582
2023
Closest in time.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu, “Viola: Object-centric imitation learning for vision-based robot manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 1199–1210
2023
Closest in time.
C. Li, S. Blaes, P. Kolev, M. Vlastelica, J. Frey, and G. Martius, “Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2944–2950
2023
Closest in time.
C. Tessler, Y. Kasten, Y. Guo, S. Mannor, G. Chechik, and X. B. Peng, “Calm: Conditional adversarial latent models for directable virtual characters,” in ACM SIGGRAPH 2023 Conference Proceedings , 2023, pp. 1–9
2023
Closest in time.