Fetching the paper…
Reading the bibliography…
Object rearrangement is the problem of enabling a robot to identify the correct object placement in a complex environment.
V. I. Levenshtein et al. , “Binary codes capable of correcting deletions, insertions, and reversals,” in Soviet physics doklady , vol. 10, no. 8. Soviet Union, 1966, pp. 707–710
1966
Earlier work this paper cites.
G. A. Miller, “WordNet: A lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
2008
Earlier work this paper cites.
R. Toris, D. Kent, and S. Chernova, “Unsupervised learning of multi-hypothesized pick-and-place task templates via crowdsourcing,” in ICRA . IEEE, 2015, pp. 4504–4510
2015
Earlier work this paper cites.
N. Abdo, C. Stachniss, L. Spinello, and W. Burgard, “Organizing objects by predicting user preferences through collaborative filtering,” IJRR , vol. 35, no. 13, pp. 1587–1608, Nov. 2016
2016
Earlier work this paper cites.
R. Speer, J. Chin, and C. Havasi, “ConceptNet 5.5: An open multilingual graph of general knowledge,” in AAAI , 2017, p. 4444–4451
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Goyal, S. Ebrahimi Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fruend, P. Yianilos, M. Mueller-Freitag et al. , “The ”Something Something” video database for learning and evaluating visual common sense,” in ICCV , 2017, pp. 5842–5850
2017
Earlier work this paper cites.
X. Dong and J. Shen, “Triplet loss in siamese network for object tracking,” in ECCV , 2018, pp. 459–474
2018
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” NeurIPS , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
——, “CLIPort: What and where pathways for robotic manipulation,” in CoRL , 2021
2021
Cited alongside, same era.
C. Paxton, C. Xie, T. Hermans, and D. Fox, “Predicting stable configurations for semantic placement of novel objects,” in CoRL . PMLR, 2022, pp. 806–815
2022
Cited alongside, same era.
C. Wang, D. Xu, and L. Fei-Fei, “Generalizable task planning through representation pretraining,” IEEE RAL , vol. 7, no. 3, 2022
2022
Cited alongside, same era.
A. Goyal, A. Mousavian, C. Paxton, Y.-W. Chao, B. Okorn, J. Deng, and D. Fox, “IFOR: Iterative flow minimization for robotic object rearrangement,” in CVPR , 2022, pp. 14 787–14 797
G. Sarch, Z. Fang, A. W. Harley, P. Schydlo, M. J. Tarr, S. Gupta, and K. Fragkiadaki, “TIDEE: Tidying up novel rooms using visuo-semantic commonsense priors,” in ECCV , 2022
2022
Later among the works it cites.
Y. Kant, A. Ramachandran, S. Yenamandra, I. Gilitschenski, D. Batra, A. Szot, and H. Agrawal, “Housekeep: Tidying virtual households using commonsense reasoning,” in ECCV . Springer, 2022, pp. 355–373
2022
Later among the works it cites.
W. Liu, C. Paxton, T. Hermans, and D. Fox, “StructFormer: Learning spatial structure for language-guided semantic rearrangement of novel objects,” in ICRA . IEEE, 2022, pp. 6322–6329
2022
Later among the works it cites.
L. Logeswaran, Y. Fu, M. Lee, and H. Lee, “Few-shot subgoal planning with language models,” in NAACL . ACL, 2022, pp. 5493–5506
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
H. Wu, J. Ye, X. Meng, C. Paxton, and G. S. Chirikjian, “Transporters with visual foresight for solving unseen rearrangement tasks,” in IROS . IEEE, 2022, pp. 10 756–10 763
2022
Cited alongside, same era.
W. Goodwin, S. Vaze, I. Havoutis, and I. Posner, “Semantically grounded object matching for robust robotic scene rearrangement,” in ICRA . IEEE, 2022, pp. 11 138–11 144
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-Actor: A multi-task transformer for robotic manipulation,” in CoRL , 2022
2022
Cited alongside, same era.
I. Kapelyukh and E. Johns, “My House, My Rules: Learning tidying preferences with graph neural networks,” in CoRL . PMLR, 2022, pp. 740–749
2022
Cited alongside, same era.
M. Wu, F. Zhong, Y. Xia, and H. Dong, “TarGF: Learning target gradient field to rearrange objects without explicit goal specification,” in NeurIPS , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 31 986–31 999
2022
Cited alongside, same era.
“Walmart.” [Online]. Available: http://www.walmart.com/
Cited in the paper.
2022
Later among the works it cites.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian et al. , “Do As I Can, Not As I Say: Grounding language in robotic affordances,” in CoRL , 2022
2022
Later among the works it cites.
W. Liu, Y. Du, T. Hermans, S. Chernova, and C. Paxton, “StructDiffusion: Language-guided creation of physically-valid structures using unseen objects,” in RSS , 2023
2023
Closest in time.
V. Jain, Y. Lin, E. Undersander, Y. Bisk, and A. Rai, “Transformers are adaptable task planners,” in CoRL , 2023
2023
Closest in time.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “VIMA: General robot manipulation with multimodal prompts,” in ICML , 2023
2023
Closest in time.