Fetching the paper…
Reading the bibliography…
The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots.
H. Lee, Y. Shen, C.-H. Yu, G. Singh, and A. Y. Ng, “Quadruped robot obstacle negotiation via reinforcement learning,” in Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE, 2006, pp. 3003–3010
2006
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.
M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch et al. , “Anymal-a highly mobile and dynamic quadrupedal robot,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2016, pp. 38–44
2016
Earlier work this paper cites.
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Nayyar, V. Puri, N. G. Nguyen, and D. N. Le, “Smart surveillance robot for real-time monitoring and control system in environment and industrial applications,” in Information Systems Design and Intelligent Applications: Proceedings of Fourth International Conference INDIA 2017 . Springer, 2018, pp. 229–243
2018
Earlier work this paper cites.
P. Fankhauser, M. Bjelonic, C. D. Bellicoso, T. Miki, and M. Hutter, “Robust rough-terrain locomotion with a quadrupedal robot,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 5761–5768
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics: Results of the 11th International Conference . Springer, 2018, pp. 621–635
2018
Earlier work this paper cites.
T. Rashid, M. Samvelyan, C. Schroeder, G. Farquhar, J. Foerster, and S. Whiteson, “Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning,” in International Conference on Machine Learning . PMLR, 2018, pp. 4295–4304
2018
Earlier work this paper cites.
A. P. Pandian, “Artificial intelligence application in smart warehousing environment for automated logistics,” Journal of Artificial Intelligence , vol. 1, no. 02, pp. 63–72, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
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, p. eaau5872, 2019
2019
Earlier work this paper cites.
M. P. Austin, M. Y. Harper, J. M. Brown, E. G. Collins, and J. E. Clark, “Navigation for legged mobility: dynamic climbing,” IEEE Transactions on Robotics , vol. 36, no. 2, pp. 537–544, 2019
2019
Earlier work this paper cites.
W. Koch, R. Mancuso, R. West, and A. Bestavros, “Reinforcement learning for uav attitude control,” ACM Transactions on Cyber-Physical Systems , vol. 3, no. 2, pp. 1–21, 2019
2019
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Cited alongside, same era.
V. Tsounis, M. Alge, J. Lee, F. Farshidian, and M. Hutter, “Deepgait: Planning and control of quadrupedal gaits using deep reinforcement learning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3699–3706, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Jin, X. Liu, Y. Shao, H. Wang, and W. Yang, “High-speed quadrupedal locomotion by imitation-relaxation reinforcement learning,” Nature Machine Intelligence , vol. 4, no. 12, pp. 1198–1208, 2022
2022
Later among the works it cites.
G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” The International Journal of Robotics Research , p. 02783649231224053, 2022
2022
Later among the works it cites.
JiDi, “Jidi olympics football,” https://github.com/jidiai/ai_lib/blob/master/env/olympics_football.py , 2022
2022
Later among the works it cites.
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. Wu, “The surprising effectiveness of ppo in cooperative multi-agent games,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 611–24 624, 2022
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…
2020
Cited alongside, same era.
2021
Cited alongside, same era.
H. Lee and J. Jeong, “Mobile robot path optimization technique based on reinforcement learning algorithm in warehouse environment,” Applied sciences , vol. 11, no. 3, p. 1209, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
F. B. Martins, M. G. Machado, H. F. Bassani, P. H. M. Braga, and E. S. Barros, “rsoccer: A framework for studying reinforcement learning in small and very small size robot soccer,” 2021
2021
Cited alongside, same era.
Y. Song, S. Naji, E. Kaufmann, A. Loquercio, and D. Scaramuzza, “Flightmare: A flexible quadrotor simulator,” in Conference on Robot Learning . PMLR, 2021, pp. 1147–1157
2021
Cited alongside, same era.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100
2022
Cited alongside, same era.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
2022
Cited alongside, same era.
2022
Later among the works it cites.
L. Wen, Y. Liu, and H. Li, “Cl-mapf: Multi-agent path finding for car-like robots with kinematic and spatiotemporal constraints,” Robotics and Autonomous Systems , vol. 150, p. 103997, 2022
2022
Later among the works it cites.
Y. Ji, G. B. Margolis, and P. Agrawal, “Dribblebot: Dynamic legged manipulation in the wild,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5155–5162
2023
Later among the works it cites.
T. N. Nguyen, T. B. Nguyen, T. Van Chien, and T. H. Nguyen, “Utilizing deep reinforcement learning to control uav movement for environmental monitoring,” International Journal of Electrical and Electronic Engineering & Telecommunications , vol. 12, no. 5, pp. 317–325, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on robot learning . PMLR, 2023, pp. 403–415
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Chen, Y. Geng, F. Zhong, J. Ji, J. Jiang, Z. Lu, H. Dong, and Y. Yang, “Bi-dexhands: Towards human-level bimanual dexterous manipulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
G. B. Margolis and P. Agrawal, “Walk these ways: Tuning robot control for generalization with multiplicity of behavior,” in Conference on Robot Learning . PMLR, 2023, pp. 22–31
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Gai, S. Lyu, H. Zhang, and D. Wang, “Continual reinforcement learning for quadruped robot locomotion,” Entropy , vol. 26, no. 1, p. 93, 2024
2024
Closest in time.