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This paper presents XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end Imitation Learning (IL) system for a whole-body autonomous humanoid robot used in real-world Human-Robot Interaction (HRI) scenarios.
2003
Earlier work this paper cites.
L. Rozo, J. Silvério, S. Calinon, and D. Caldwell, “Learning controllers for reactive and proactive behaviors in human-robot collaboration,” Frontiers in Robotics and AI , vol. 3, pp. 1–11, 06 2016
2016
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T. Zhang, Z. McCarthy, O. Jow, D. Lee, K. Goldberg, and P. Abbeel, “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” in IEEE International Conference on Robotics and Automation , 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:3720790
2017
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C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” 2017
2017
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R. Rahmatizadeh, P. Abolghasemi, L. Bölöni, and S. Levine, “Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE Press, 2018, p. 3758–3765. [Online]. Available: https://doi.org/10.1109/ICRA.2018.8461076
2018
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K. Darvish, Y. Tirupachuri, G. Romualdi, L. Rapetti, D. Ferigo, F. J. A. Chavez, and D. Pucci, “Whole-body geometric retargeting for humanoid robots,” in 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) , 2019, pp. 679–686
2019
Earlier work this paper cites.
Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. López, “Multimodal end-to-end autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 1, p. 537–547, jan 2022. [Online]. Available: https://doi.org/10.1109/TITS.2020.3013234
2020
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2020
Earlier work this paper cites.
G. Romualdi, S. Dafarra, Y. Hu, P. Ramadoss, F. J. A. Chavez, S. Traversaro, and D. Pucci, “A benchmarking of dcm-based architectures for position, velocity and torque-controlled humanoid robots,” International Journal of Humanoid Robotics , vol. 17, no. 01, p. 1950034, 2020
2020
Cited alongside, same era.
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipulation,” in 5th Annual Conference on Robot Learning , 2021. [Online]. Available: https://openreview.net/forum?id=JrsfBJtDFdI
2021
Cited alongside, same era.
T. Ogata, K. Takahashi, T. Yamada, S. Murata, and K. Sasaki, “Machine Learning for Cognitive Robotics,” in Cognitive Robotics . The MIT Press, 05 2022. [Online]. Available: https://doi.org/10.7551/mitpress/13780.003.0014
2022
Cited alongside, same era.
F. Semeraro, A. Griffiths, and A. Cangelosi, “Human–robot collaboration and machine learning: A systematic review of recent research,” Robotics and Computer-Integrated Manufacturing , vol. 79, p. 102432, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0736584522001156
2023
Later among the works it cites.
J. Luo, W. Liu, W. Qi, J. Hu, J. Chen, and C. Yang, “A vision-based virtual fixture with robot learning for teleoperation,” Robotics and Autonomous Systems , vol. 164, p. 104414, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0921889023000532
2023
Later among the works it cites.
M. Seo, S. Han, K. Sim, S. H. Bang, C. Gonzalez, L. Sentis, and Y. Zhu, “Deep imitation learning for humanoid loco-manipulation through human teleoperation,” in IEEE-RAS International Conference on Humanoid Robots (Humanoids) , 2023
2023
Later among the works it cites.
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R. Pérez-Dattari, B. Brito, O. de Groot, J. Kober, and J. Alonso-Mora, “Visually-guided motion planning for autonomous driving from interactive demonstrations,” Engineering Applications of Artificial Intelligence , vol. 116, p. 105277, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197622003323
2022
Cited alongside, same era.
S. Nasiriany, T. Gao, A. Mandlekar, and Y. Zhu, “Learning and retrieval from prior data for skill-based imitation learning,” 2022
2022
Cited alongside, same era.
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu, “Viola: Imitation learning for vision-based manipulation with object proposal priors,” 6th Annual Conference on Robot Learning (CoRL) , 2022
2022
Cited alongside, same era.
K. Darvish, L. Penco, J. Ramos, R. Cisneros, J. Pratt, E. Yoshida, S. Ivaldi, and D. Pucci, “Teleoperation of humanoid robots: A survey,” IEEE Transactions on Robotics , vol. PP, 06 2023
2023
Cited alongside, same era.
“ifeel.” [Online]. Available: https://ifeeltech.eu/
Cited in the paper.
“ergocub.” [Online]. Available: https://ergocub.eu/
Cited in the paper.
2023
Later among the works it cites.
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
2023
Later among the works it cites.
A. Obaigbena, O. A. Lottu, E. D. Ugwuanyi, B. S. Jacks, E. O. Sodiya, and O. D. Daraojimba, “Ai and human-robot interaction: A review of recent advances and challenges,” GSC Advanced Research and Reviews , 2024. [Online]. Available: https://gsconlinepress.com/journals/gscarr/content/ai-and-human-robot-interaction-review-recent-advances-and-challenges
2024
Closest in time.
S. Dafarra, U. Pattacini, G. Romualdi, L. Rapetti, R. Grieco, K. Darvish, G. Milani, E. Valli, I. Sorrentino, P. M. Viceconte, A. Scalzo, S. Traversaro, C. Sartore, M. Elobaid, N. Guedelha, C. Herron, A. Leonessa, F. Draicchio, G. Metta, M. Maggiali, and D. Pucci, “icub3 avatar system: Enabling remote fully immersive embodiment of humanoid robots,” Science Robotics , vol. 9, no. 86, p. eadh3834, 2024. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.adh3834
2024
Closest in time.