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We propose Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation (Bi-VLA), a novel framework that extends bilateral control-based imitation learning to handle more than one task within a single model.
1905
Earlier work this paper cites.
K. Ohnishi, M. Shibata, and T. Murakami, “Motion control for advanced mechatronics,” IEEE/ASME transactions on mechatronics , vol. 1, no. 1, pp. 56–67, 1996
1996
Earlier work this paper cites.
T. Murakami, F. Yu, and K. Ohnishi, “Torque sensorless control in multidegree-of-freedom manipulator,” IEEE Transactions on Industrial Electronics , vol. 40, no. 2, pp. 259–265, 2002
2002
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Adachi, K. Fujimoto, S. Sakaino, and T. Tsuji, “Imitation learning for object manipulation based on position/force information using bilateral control,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 3648–3653
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Proceedings of the 5th Conference on Robot Learning (CoRL) , 2021
2021
Earlier work this paper cites.
S. Sakaino, K. Fujimoto, Y. Saigusa, and T. Tsuji, “Imitation learning for variable speed contact motion for operation up to control bandwidth,” IEEE Open Journal of the Industrial Electronics Society , vol. 3, pp. 116–127, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
K. Hayashi, S. Sakaino, and T. Tsuji, “An independently learnable hierarchical model for bilateral control-based imitation learning applications,” IEEE Access , vol. 10, pp. 32 766–32 781, 2022
2022
Earlier work this paper cites.
H. He, C.-l. Lu, Y. Wen, G. Saunders, P. Yang, J. Schoonover, J. Wason, A. Julius, and J. T. Wen, “High-speed high-accuracy spatial curve tracking using motion primitives in industrial robots,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 12 289–12 295
2023
Cited alongside, same era.
Z. Huang, Y.-J. Mun, X. Li, Y. Xie, N. Zhong, W. Liang, J. Geng, T. Chen, and K. Driggs-Campbell, “Hierarchical intention tracking for robust human-robot collaboration in industrial assembly tasks,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 9821–9828
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Later among the works it cites.
M. Verghese and C. Atkeson, “Skills made to order: Efficient acquisition of robot cooking skills guided by multiple forms of internet data,” in 2025 IEEE International Conference on Robotics and Automation (ICRA) , 2025, pp. 11 965–11 971
2025
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K.-M. Yang, J. Koo, and K.-H. Seo, “Development of contactless delivery service robot with modular working platform in isolation wards,” in 2025 IEEE International Conference on Robotics and Automation (ICRA) , 2025, pp. 4316–4321
2025
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2025
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2023
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2024
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T. Buamanee, M. Kobayashi, Y. Uranishi, and H. Takemura, “Bi-act: Bilateral control-based imitation learning via action chunking with transformer,” in 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) . IEEE, 2024, pp. 410–415
2024
Cited alongside, same era.
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2025
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N. Masuya, H. Sato, K. Yamane, T. Kusume, S. Sakaino, and T. Tsuji, “Variable-speed teaching–playback as real-world data augmentation for imitation learning,” Advanced Robotics , vol. 39, no. 10, pp. 550–565, 2025. [Online]. Available: https://doi.org/10.1080/01691864.2025.2497423
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T. Tsuji, “Mamba as a motion encoder for robotic imitation learning,” IEEE Access , vol. 13, pp. 69 941–69 949, 2025
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M. Kobayashi, T. Buamanee, and T. Kobayashi, “ALPHA- α \alpha and bi-act are all you need: Importance of position and force information/ control for imitation learning of unimanual and bimanual robotic manipulation with low-cost system,” IEEE Access , vol. 13, pp. 29 886–29 899, 2025
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M. Kobayashi, T. Buamanee, and Y. Uranishi, “Dabi: Evaluation of data augmentation methods using downsampling in bilateral control-based imitation learning with images,” in 2025 IEEE International Conference on Robotics and Automation (ICRA) , 2025, pp. 16 892–16 898
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