Fetching the paper…
Reading the bibliography…
We propose M3Bench, a new benchmark for whole-body motion generation in mobile manipulation tasks.
J. J. Kuffner and S. M. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in International Conference on Robotics and Automation (ICRA)
2000
Earlier work this paper cites.
J. Schulman, J. Ho, A. X. Lee, I. Awwal, H. Bradlow, and P. Abbeel, “Finding locally optimal, collision-free trajectories with sequential convex optimization.,” in Robotics: Science and Systems (RSS)
2013
Earlier work this paper cites.
J. Schulman, Y. Duan, J. Ho, A. Lee, I. Awwal, H. Bradlow, J. Pan, S. Patil, K. Goldberg, and P. Abbeel, “Motion planning with sequential convex optimization and convex collision checking,” International Journal of Robotics Research (IJRR)
2014
Earlier work this paper cites.
J. Leitner, A. W. Tow, N. Sünderhauf, J. E. Dean, J. W. Durham, M. Cooper, M. Eich, C. Lehnert, R. Mangels, C. McCool, et al
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems (NeurIPS)
2017
Earlier work this paper cites.
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in Conference on Computer Vision and Pattern Recognition (CVPR)
2018
Earlier work this paper cites.
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters (RA-L)
2020
Earlier work this paper cites.
M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox, “ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks,” in Conference on Computer Vision and Pattern Recognition (CVPR)
2020
Earlier work this paper cites.
Z. Jiao, Z. Zhang, X. Jiang, D. Han, S.-C. Zhu, Y. Zhu, and H. Liu, “Consolidating kinematic models to promote coordinated mobile manipulations,” in International Conference on Intelligent Robots and Systems (IROS)
2021
Earlier work this paper cites.
K. Mo, Y. Qin, F. Xiang, H. Su, and L. Guibas, “O2O-Afford: Annotation-free large-scale object-object affordance learning,” in Conference on Robot Learning (CoRL)
2021
Earlier work this paper cites.
M. Han, Z. Zhang, Z. Jiao, X. Xie, Y. Zhu, S.-C. Zhu, and H. Liu, “Reconstructing interactive 3d scenes by panoptic mapping and cad model alignments,” in International Conference on Robotics and Automation (ICRA)
2021
Earlier work this paper cites.
Z. Jiao, Z. Zhang, W. Wang, D. Han, S.-C. Zhu, Y. Zhu, and H. Liu, “Efficient task planning for mobile manipulation: a virtual kinematic chain perspective,” in International Conference on Intelligent Robots and Systems (IROS)
2021
Earlier work this paper cites.
T. Mu, Z. Ling, F. Xiang, D. Yang, X. Li, X. Li, S. Tao, Z. Huang, Z. Jia, and H. Su, “Maniskill: Generalizable manipulation skill benchmark with large-scale demonstrations,” in Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks
2021
Earlier work this paper cites.
C. Chamzas, C. Quintero-Pena, Z. Kingston, A. Orthey, D. Rakita, M. Gleicher, M. Toussaint, and L. E. Kavraki, “Motionbenchmaker: A tool to generate and benchmark motion planning datasets,” IEEE Robotics and Automation Letters (RA-L)
2021
Earlier work this paper cites.
A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. S. Chaplot, O. Maksymets, et al
2021
Cited alongside, same era.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al
2021
Cited alongside, same era.
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, et al
2021
Cited alongside, same era.
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” in Advances in Neural Information Processing Systems (NeurIPS)
2021
Cited alongside, same era.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in International Conference on Computer Vision (ICCV)
2021
P. Li, T. Liu, Y. Li, Y. Geng, Y. Zhu, Y. Yang, and S. Huang, “Gendexgrasp: Generalizable dexterous grasping,” in International Conference on Robotics and Automation (ICRA)
2023
Later among the works it cites.
P. Xie, R. Chen, S. Chen, Y. Qin, F. Xiang, T. Sun, J. Xu, G. Wang, and H. Su, “Part-Guided 3D RL for Sim2Real Articulated Object Manipulation,” in IEEE Robotics and Automation Letters (RA-L)
2023
Later among the works it cites.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in International Conference on Computer Vision (ICCV)
2023
Later among the works it cites.
J. Urain, N. Funk, J. Peters, and G. Chalvatzaki, “Se(3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion,” in International Conference on Robotics and Automation (ICRA)
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Han, Z. Zhang, Z. Jiao, X. Xie, Y. Zhu, S.-C. Zhu, and H. Liu, “Scene reconstruction with functional objects for robot autonomy,” International Journal of Computer Vision (IJCV)
2022
Cited alongside, same era.
J. Gu, D. S. Chaplot, H. Su, and J. Malik, “Multi-skill mobile manipulation for object rearrangement,” in International Conference on Learning Representations (ICLR)
2022
Cited alongside, same era.
Z. Zhang, Z. Jiao, W. Wang, Y. Zhu, S.-C. Zhu, and H. Liu, “Understanding physical effects for effective tool-use,” IEEE Robotics and Automation Letters (RA-L)
2022
Cited alongside, same era.
J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, Y. Tang, S. Tao, X. Wei, Y. Yao, et al
2022
Cited alongside, same era.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters (RA-L)
2022
Cited alongside, same era.
S. Srivastava, C. Li, M. Lingelbach, R. Martín-Martín, F. Xia, K. E. Vainio, Z. Lian, C. Gokmen, S. Buch, K. Liu, et al
2022
Cited alongside, same era.
K. Zheng, X. Chen, O. C. Jenkins, and X. Wang, “Vlmbench: A compositional benchmark for vision-and-language manipulation,” in Advances in Neural Information Processing Systems (NeurIPS)
2022
Cited alongside, same era.
A. Fishman, A. Murali, C. Eppner, B. Peele, B. Boots, and D. Fox, “Motion policy networks,” in Conference on Robot Learning (CoRL)
2023
Later among the works it cites.
Z. Zhang, M. Han, B. Jia, Z. Jiao, Y. Zhu, S.-C. Zhu, and H. Liu, “Learning a causal transition model for object cutting,” in International Conference on Intelligent Robots and Systems (IROS)
2023
Later among the works it cites.
R. Gong, J. Huang, Y. Zhao, H. Geng, X. Gao, Q. Wu, W. Ai, Z. Zhou, D. Terzopoulos, S.-C. Zhu, et al
2023
Later among the works it cites.
X. Huang, D. Batra, A. Rai, and A. Szot, “Skill transformer: A monolithic policy for mobile manipulation,” in International Conference on Computer Vision (ICCV)
2023
Later among the works it cites.
S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3d scenes,” in Conference on Computer Vision and Pattern Recognition (CVPR)
2023
Later among the works it cites.
S. Wang, M. Han, Z. Jiao, Z. Zhang, Y. N. Wu, S.-C. Zhu, and H. Liu, “Llm3: Large language model-based task and motion planning with motion failure reasoning,” in International Conference on Intelligent Robots and Systems (IROS)
2024
Closest in time.
2024
Closest in time.
Y. Yang, B. Jia, P. Zhi, and S. Huang, “Physcene: Physically interactable 3d scene synthesis for embodied ai,” in Conference on Computer Vision and Pattern Recognition (CVPR)
2024
Closest in time.
S. Yan, Z. Zhang, M. Han, Z. Wang, Q. Xie, Z. Li, Z. Li, H. Liu, X. Wang, and S.-C. Zhu, “M2diffuser: Diffusion-based trajectory optimization for mobile manipulation in 3d scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2025
Closest in time.
H. Liu, Q. Xie, Z. Zhang, T. Yuan, S. Wang, Z. Wang, X. Leng, L. Sun, J. Zhang, Z. He, and Y. Su, “Pr2: A physics- and photo-realistic humanoid testbed with pilot study in competition,” Journal of Field Robotics
2025
Closest in time.