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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 02001 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 02001
Number of page(s) 16
Section Intelligent Automation
DOI https://doi.org/10.1051/epjconf/202636702001
Published online 29 April 2026
  1. Mnih V, Kavukcuoglu K, Silver D, et al (2015) Human-level control through deep reinforcement learning. Nature 2015 518:7540 518:529–533. https://doi.org/10.1038/nature14236 [Google Scholar]
  2. Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Rob Auton Syst 57:469–483. https://doi.org/10.1016/J.ROBOT.2008.10.024 [Google Scholar]
  3. Ross S, Gordon GJ, Bagnell JA (2010) A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. Journal of Machine Learning Research 15:627–635 [Google Scholar]
  4. Fang B, Jia S, Guo D, et al (2019) Survey of imitation learning for robotic manipulation. International Journal of Intelligent Robotics and Applications 2019 3:4 3:362–369. https://doi.org/10.1007/s41315-019-00103-5 [Google Scholar]
  5. Chi C, Xu Z, Feng S, et al (2023) Diffusion Policy: Visuomotor Policy Learning via Action Diffusion. International Journal of Robotics Research 44:1684–1704. https://doi.org/10.1177/02783649241273668 [Google Scholar]
  6. Kang JH, Joshi S, Huang R, Gupta SK (2025) Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations arXiv preprint arXiv:2503.03998. https://doi.org/10.48550/arXiv.2503.03998 [Google Scholar]
  7. Wolf R, Shi Y, Liu S, Rayyes R (2025) Diffusion models for robotic manipulation: a survey. Front Robot AI 12:1606247. https://doi.org/10.3389/frobt.2025.1606247 [Google Scholar]
  8. Driess D, Xia F, Sajjadi MSM, et al (2023) PaLM-E: An Embodied Multimodal Language Model. arXiv preprint arXiv:2303.03378 [Google Scholar]
  9. Brohan A, Brown N, Carbajal J, et al (2023) RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. arXiv preprint arXiv:2307.15818 [Google Scholar]
  10. Liang J, Huang W, Xia F, et al (2022) Code as Policies: Language Model Programs for Embodied Control. Proc IEEE Int Conf Robot Autom 2023-May:9493–9500. https://doi.org/10.1109/ICRA48891.2023.10160591 [Google Scholar]
  11. Ahn M, Brohan A, Brown N, et al (2022) Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. Proc Mach Learn Res 205:287–318 [Google Scholar]
  12. Xiao X, Liu J, Wang Z, et al (2023) Robot Learning in the Era of Foundation Models: A Survey. arXiv preprint arXiv:2311.14379 [Google Scholar]
  13. Tao F, Zhang M (2017) Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE Access 5:20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069 [Google Scholar]
  14. Rehman S, Al-Greer M, Burn AS, et al (2025) High-Volume Battery Recycling: Technical Review of Challenges and Future Directions. Batteries 2025, Vol 11, Page 94 11:94. https://doi.org/10.3390/BATTERIES11030094 [Google Scholar]
  15. ISO 10218-2:2025 - Robotics — Safety requirements — Part 2: Industrial robot applications and robot cells. https://www.iso.org/standard/73934.html. Accessed 13 Jan 2026 [Google Scholar]
  16. Ames AD, Xu X, Grizzle JW, Tabuada P (2016) Control Barrier Function Based Quadratic Programs for Safety Critical Systems. IEEE Trans Automat Contr 62:3861–3876. https://doi.org/10.1109/TAC.2016.2638961 [Google Scholar]
  17. Wabersich KP, Zeilinger MN (2021) A predictive safety filter for learning-based control of constrained nonlinear dynamical systems. Automatica 129:109597. https://doi.org/10.1016/J.AUTOMATICA.2021.109597 [Google Scholar]
  18. Fuller A, Fan Z, Day C, Barlow C (2020) Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access 8:108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358 [Google Scholar]
  19. Lee J, Bagheri B, Kao HA (2015) A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18–23. https://doi.org/10.1016/j.mfglet.2014.12.001 [CrossRef] [Google Scholar]
  20. Bommasani R, Hudson DA, Adeli E, et al (2022) On the Opportunities and Risks of Foundation Models [Google Scholar]
  21. Ablett T, Limoyo O, Sigal A, et al (2025) Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor. IEEE Transactions on Robotics 41:946–959. https://doi.org/10.1109/TRO.2024.3521864 [Google Scholar]
  22. Brunke L, Greeff M, Hall AW, et al (2021) Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning [Google Scholar]
  23. Amodei D, Olah C, Steinhardt J, Christiano P, Schulman J, Mané D (2016) Concrete Problems in AI Safety. arXiv preprint arXiv:1606.06565. https://doi.org/10.48550/arXiv.1606.06565 [Google Scholar]
  24. Yan Z, Jouandeau N, Cherif AA (2013) A survey and analysis of multi-robot coordination. Int J Adv Robot Syst 10:. https://doi.org/10.5772/57313 [Google Scholar]
  25. Gielis J, Shankar A, Prorok A (2022) A Critical Review of Communications in Multi-robot Systems. Current Robotics Reports 3:213–225. https://doi.org/10.1007/S43154-022-00090-9 [Google Scholar]
  26. Lerman K, Jones C, Galstyan A, Mataríc MJ (2006) Analysis of Dynamic Task Allocation in Multi-Robot Systems. International Journal of Robotics Research 25:225–241. https://doi.org/10.1177/0278364906063426 [Google Scholar]
  27. Erdogan C, Contreras CA, Stolkin R, Rastegarpanah A (2024) Multi-Robot Task Planning for Efficient Battery Disassembly in Electric Vehicles. Robotics 2024, Vol 13, Page 75 13:75. https://doi.org/10.3390/ROBOTICS13050075 [Google Scholar]

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