Fetching the paper…
Reading the bibliography…
The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” Advances in NIPS , 1988
1988
Earlier work this paper cites.
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE TRO , 1996
1996
Earlier work this paper cites.
S. M. LaValle, J. J. Kuffner, B. Donald, et al. , “Rapidly-exploring random trees: Progress and prospects,” Algorithmic and computational robotics: new directions , 2001
2001
Earlier work this paper cites.
M. Wang and J. N. Liu, “Fuzzy logic-based real-time robot navigation in unknown environment with dead ends,” RAS , 2008
2008
Earlier work this paper cites.
D. Harabor and A. Grastien, “Online graph pruning for pathfinding on grid maps,” in AAAI , 2011
2011
Earlier work this paper cites.
C. Rösmann, W. Feiten, T. Wösch, F. Hoffmann, and T. Bertram, “Trajectory modification considering dynamic constraints of autonomous robots,” in ROBOTIK , 2012
2012
Earlier work this paper cites.
K. Kurzer, “Path planning in unstructured environments: A real-time hybrid a* implementation for fast and deterministic path generation for the kth research concept vehicle,” Master’s thesis, 2016
2016
Earlier work this paper cites.
B. et al., “End to end learning for self-driving cars,” arXiv:1604.07316 [cs] , 2016
2016
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in NIPS , 2020
2020
Earlier work this paper cites.
J. Peng, Y. Chen, Y. Duan, Y. Zhang, J. Ji, and Y. Zhang, “Towards an online rrt-based path planning algorithm for ackermann-steering vehicles,” in IEEE ICRA , 2021
2021
Earlier work this paper cites.
S. Yao, G. Chen, Q. Qiu, J. Ma, X. Chen, and J. Ji, “Crowd-aware robot navigation for pedestrians with multiple collision avoidance strategies via map-based deep reinforcement learning,” in IEEE/RSJ IROS , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” Advances in NIPS , 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
G. You, X. Chu, Y. Duan, J. Peng, J. Ji, Y. Zhang, and Y. Zhang, “P 3 o: Transferring visual representations for reinforcement learning via prompting,” in IEEE ICME , 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Yokoyama, S. Ha, and D. Batra, “Success weighted by completion time: A dynamics-aware evaluation criteria for embodied navigation,” in IEEE/RSJ IROS , 2021
2021
Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: A survey,” Autonomous Robots , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
2022
Cited alongside, same era.
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “Implicit behavioral cloning,” in CoRL . PMLR, 2022
2022
Cited alongside, same era.
J. Carvalho, A. T. Le, M. Baierl, D. Koert, and J. Peters, “Motion planning diffusion: Learning and planning of robot motions with diffusion models,” in IEEE/RSJ IROS , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Song, P. Dhariwal, M. Chen, and I. Sutskever, “Consistency models,” 2023
2023
Later among the works it cites.
C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in arXiv , 2024
2024
Closest in time.
H. He, C. Bai, K. Xu, Z. Yang, W. Zhang, D. Wang, B. Zhao, and X. Li, “Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning,” Advances in NIPS , 2024
2024
Closest in time.