Fetching the paper…
Reading the bibliography…
Underwater environments pose unique challenges for robotic navigation and manipulation.
A. Mallios, E. Vidal, R. Campos, and M. Carreras, “Underwater caves sonar data set,” Int. J. Robot. Res. , vol. 36, no. 12, pp. 1247–1251, Oct. 2017
2017
Earlier work this paper cites.
R. K. Katzschmann, J. DelPreto, R. MacCurdy, and D. Rus, “Exploration of underwater life with an acoustically controlled soft robotic fish,” Sci. Robot. , vol. 3, no. 16, Mar. 2018, Art. no. eaar3449
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Ferrera, V. Creuze, J. Moras, and P. Trouvé-Peloux, “Aqualoc: An underwater dataset for visual–inertial–pressure localization,” Int. J. Robot. Res. , vol. 38, no. 14, pp. 1549–1559, Dec. 2019
2019
Earlier work this paper cites.
P. Cieslak, “Stonefish: An advanced open–source simulation tool designed for marine robotics, with a ROS interface,” in Proc. OCEANS 2019 – Marseille , Jun. 2019, pp. 1–6
2019
Earlier work this paper cites.
C. Li et al. , “An underwater image enhancement benchmark dataset and beyond,” IEEE Trans. on Image Process. , vol. 29, pp. 4376–4389, 2020
2020
Earlier work this paper cites.
A. Gomez Chavez, A. Ranieri, D. Chiarella, and A. Birk, “Underwater vision-based gesture recognition: A robustness validation for safe human–robot interaction,” IEEE Robot. Automat. Mag. , vol. 28, no. 3, pp. 67–78, Sep. 2021
2021
Earlier work this paper cites.
D. Singh and M. Valdenegro-Toro, “The marine debris dataset for forward-looking sonar semantic segmentation,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. Workshops (ICCV) , Montreal, Canada, Oct. 2021, pp. 3741–3749
2021
Earlier work this paper cites.
P. Georg Olofsson Zwilgmeyer, M. Yip, A. Langeland Teigen, R. Mester, and A. Stahl, “The VAROS synthetic underwater data set: Towards realistic multi-sensor underwater data with ground truth,” in Proc. 2021 IEEE/CVF Int. Conf. Comput. Vis. Workshops (ICCV) , Montreal, Canada, Oct. 2021, pp. 3715–3723
2021
Earlier work this paper cites.
C. Hu, S. Zhu, Y. Liang, and W. Song, “Tightly-coupled visual-inertial-pressure fusion using forward and backward IMU preintegration,” IEEE Robot. Autom. Lett. , vol. 7, no. 3, pp. 6790–6797, Jul. 2022
2022
Earlier work this paper cites.
Y. Wang et al. , “Target tracking control of a biomimetic underwater vehicle through deep reinforcement learning,” IEEE Trans. Neural Netw. Learning Syst. , vol. 33, no. 8, pp. 3741–3752, Aug. 2022
2022
Earlier work this paper cites.
O. Álvarez-Tuñón et al. , “MIMIR-UW: A multipurpose synthetic dataset for underwater navigation and inspection,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) , Detroit, MI, USA, Oct. 2023, pp. 6141–6148
2023
Earlier work this paper cites.
F. Bai, H. Zhang, T. Tao, Z. Wu, Y. Wang, and B. Xu, “Picor: Multi-task deep reinforcement learning with policy correction,” in Proc. AAAI Conf. Artif. Intell. , Washington, DC, USA, Jun. 2023, pp. 6728–6736
2023
Earlier work this paper cites.
J. Yang, J. Ni, M. Xi, J. Wen, and Y. Li, “Intelligent path planning of underwater robot based on reinforcement learning,” IEEE Trans. Automat. Sci. Eng. , vol. 20, no. 3, pp. 1983–1996, Jul. 2023
2023
Cited alongside, same era.
I. Masmitja et al. , “Dynamic robotic tracking of underwater targets using reinforcement learning,” Sci. Robot. , vol. 8, no. 80, Jul. 2023, Art. no. eade7811
2023
Cited alongside, same era.
B. Zitkovich et al. , “RT-2: Vision-language-action models transfer web knowledge to robotic control,” in Proc. 7th Conf. Robot Learning (CoRL) , vol. 229, Atlanta, GA, USA, Nov. 2023, pp. 2165–2183
2023
Cited alongside, same era.
O. Álvarez-Tuñón, L. R. Marnet, M. Aubard, L. Antal, M. Costa, and Y. Brodskiy, “SubPipe: A submarine pipeline inspection dataset for segmentation and visual-inertial localization,” in OCEANS 2024 - Singapore , Apr. 2024, pp. 1–7
2024
Cited alongside, same era.
T. Liu et al. , “A bioinspired multimotion modality underwater microrobot,” Sci. Adv. , vol. 11, no. 19, May 2025, Art. no. eadu2527
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
X. Tong et al. , “Quart–online: Latency–free multimodal large language model for quadruped robot learning,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , Atlanta, GA, USA, May 2025, pp. 9533–9539
2025
Closest in time.
W. Xu et al. , “NAUTILUS: A large multimodal model for underwater scene understanding,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , San Diego, USA, Dec. 2025
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Phung, G. Billings, A. F. Daniele, M. R. Walter, and R. Camilli, “A shared autonomy system for precise and efficient remote underwater manipulation,” IEEE Trans. Robot. , vol. 40, pp. 4147–4159, 2024
2024
Cited alongside, same era.
E. Palmer, C. Holm, and G. Hollinger, “Angler: An autonomy framework for intervention tasks with lightweight underwater vehicle manipulator systems,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , Yokohama, Japan, May 2024, pp. 6126–6132
2024
Cited alongside, same era.
2024
Cited alongside, same era.
X. Lin et al. , “Uivnav: Underwater information–driven vision–based navigation via imitation learning,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , Yokohama, Japan, May 2024, pp. 5250–5256
2024
Cited alongside, same era.
J. Gao, Y. Li, Y. Chen, Y. He, and J. Guo, “An improved SAC–based deep reinforcement learning framework for collaborative pushing and grasping in underwater environments,” IEEE Trans. Instrum. Meas. , vol. 73, pp. 1–14, 2024
2024
Cited alongside, same era.
K. Black et al. , “ π \pi 0: A vision–language–action flow model for general robot control,” in Proc. Robot.: Sci. Syst. (RSS) , Delft, Netherlands, Nov. 2024
2024
Cited alongside, same era.
A. O’Neill et al. , “Open X-embodiment: Robotic learning datasets and RT-X models,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , Yokohama, Japan, May 2024, pp. 6892–6903
2024
Cited alongside, same era.
X. Chu et al. , “A multimodal optical dataset for underwater image enhancement, detection, segmentation, and reconstruction,” Sci. Data , vol. 12, no. 1, p. 1554, Sep. 2025
2025
Cited alongside, same era.
2025
Closest in time.
R. Liu, H. Ha, M. Hou, S. Song, and C. Vondrick, “Self–improving autonomous underwater manipulation,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , May 2025, pp. 16 915–16 922
2025
Closest in time.
Z. Fang, T. Chen, T. Shen, D. Jiang, Z. Zhang, and G. Li, “Multi–agent generative adversarial interactive self–imitation learning for AUV formation control and obstacle avoidance,” IEEE Robot. Autom. Lett. , vol. 10, no. 5, pp. 4356–4363, May 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
M. Grimaldi et al. , “Stonefish: Supporting machine learning research in marine robotics,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA) , Atlanta, GA, USA, May 2025, pp. 13 605–13 611
2025
Closest in time.
X. Sun et al. , “Autofly: Vision-language-action model for UAV autonomous navigation in the wild,” in Proc. Int. Conf. Learn. Representations (ICLR) , Rio de Janeiro, Brazil, Feb. 2026
2026
Closest in time.