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Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances.
A. Myers, C. L. Teo, C. Fermüller, and Y. Aloimonos, “Affordance detection of tool parts from geometric features,” in
2015
Earlier work this paper cites.
L. P. Kaelbling, “The foundation of efficient robot learning,”
2020
Earlier work this paper cites.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su, “SAPIEN: A simulated part-based interactive environment,” in
2020
Earlier work this paper cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Earlier work this paper cites.
K. Mo, L. J. Guibas, M. Mukadam, A. Gupta, and S. Tulsiani, “Where2act: From pixels to actions for articulated 3d objects,” in
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in
2022
Earlier work this paper cites.
K. Zheng, X. Chen, O. C. Jenkins, and X. Wang, “Vlmbench: A compositional benchmark for vision-and-language manipulation,”
2022
Earlier work this paper cites.
Z. Xu, Z. He, and S. Song, “Universal manipulation policy network for articulated objects,”
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
H. Geng, H. Xu, C. Zhao, C. Xu, L. Yi, S. Huang, and H. Wang, “Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts,” in
2023
Earlier work this paper cites.
A. Guo, B. Wen, J. Yuan, J. Tremblay, S. Tyree, J. Smith, and S. Birchfield, “Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions,” in
2023
Earlier work this paper cites.
Z. Lin, C. Liu, R. Zhang, P. Gao, L. Qiu, H. Xiao, H. Qiu, C. Lin, W. Shao, K. Chen
2023
Earlier work this paper cites.
T. Wu, J. Zhang, X. Fu, Y. Wang, J. Ren, L. Pan, W. Wu, L. Yang, J. Wang, C. Qian
2023
Earlier work this paper cites.
P.-L. Guhur, S. Chen, R. G. Pinel, M. Tapaswi, I. Laptev, and C. Schmid, “Instruction-driven history-aware policies for robotic manipulations,” in
2023
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in
2023
Cited alongside, same era.
J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “Tidybot: Personalized robot assistance with large language models,”
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Closest in time.
X. Li, M. Zhang, Y. Geng, H. Geng, Y. Long, Y. Shen, R. Zhang, J. Liu, and H. Dong, “Manipllm: Embodied multimodal large language model for object-centric robotic manipulation,” in
2024
Closest in time.
2024
Closest in time.
D. Tola and P. Corke, “Understanding urdf: A dataset and analysis,”
2024
Closest in time.
X. Lai, Z. Tian, Y. Chen, Y. Li, Y. Yuan, S. Liu, and J. Jia, “Lisa: Reasoning segmentation via large language model,” in
2024
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2023
Cited alongside, same era.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in
2023
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo
2023
Cited alongside, same era.
Closest in time.
J. Gao, B. Sarkar, F. Xia, T. Xiao, J. Wu, B. Ichter, A. Majumdar, and D. Sadigh, “Physically grounded vision-language models for robotic manipulation,” in
2024
Closest in time.
2024
Closest in time.
Y. You, K. Xiong, Z. Yang, Z. Huang, J. Zhou, R. Shi, Z. Fang, A. W. Harley, L. Guibas, and C. Lu, “Pace: Pose annotations in cluttered environments,” Springer, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
C. Xiong, C. Shen, X. Li, K. Zhou, J. Liu, R. Wang, and H. Dong, “Autonomous interactive correction mllm for robust robotic manipulation,” in
2024
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,”
2024
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P. Gao, R. Zhang, C. Liu, L. Qiu, S. Huang, W. Lin, S. Zhao, S. Geng, Z. Lin, P. Jin
2024
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W. Xia, D. Wang, X. Pang, Z. Wang, B. Zhao, D. Hu, and X. Li, “Kinematic-aware prompting for generalizable articulated object manipulation with llms,” in
2080
Closest in time.