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Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly.
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2021
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C. Willibald and D. Lee, “Multi-level task learning based on intention and constraint inference for autonomous robotic manipulation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 7688–7695
2022
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2022
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C. Chi et al., “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , p. 02783649241273668, 2023
2023
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A. Brohan et al., “RT-1: Robotics Transformer for Real-World Control at Scale,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, 7 2023
2023
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2023
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2023
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2023
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H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen-Estruch, A. W. He, V. Myers, M. J. Kim, M. Du et al. , “Bridgedata v2: A dataset for robot learning at scale,” in Conference on Robot Learning . PMLR, 2023, pp. 1723–1736
2023
Cited alongside, same era.
R. Sinha, A. Elhafsi, C. Agia, M. Foutter, E. Schmerling, and M. Pavone, “Real-Time Anomaly Detection and Reactive Planning with Large Language Models,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, 7 2024
2024
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2024
Later among the works it cites.
2024
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A. Khazatsky et al., “DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset,” July 2024
2024
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H Fang et al., “Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 653–660
2024
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D. Sliwowski and D. Lee, “Conditionnet: Learning preconditions and effects for execution monitoring,” IEEE Robotics and Automation Letters , 2024
2024
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A. O’Neill et. al., “Open x-embodiment: Robotic learning datasets and rt-x models : Open x-embodiment collaboration0,” in IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 6892–6903
2024
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C. Higuera et al., “Sparsh: Self-supervised touch representations for vision-based tactile sensing,” 2024
2024
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D. Gehrig and D. Scaramuzza, “Low-latency automotive vision with event cameras,” Nature , vol. 629, no. 8014, pp. 1034–1040, 2024
2024
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C. Chi et al., “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” RSS , 2024
2024
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T. Reinold, S. Ghosh, and G. Gallego, “Combined physics and event camera simulator for slip detection,” in Proceedings of the Winter Conference on Applications of Computer Vision , 2025, pp. 935–943
2025
Closest in time.
D. Sliwowski and D. Lee, “M2R2: Mulitmodal robotic representation for temporal action segmentation,” in Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2025
2025
Closest in time.