LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusor

J. R. Dormand and P. J. Prince, “A family of embedded runge-kutta formulae,” Journal of computational and applied mathematics , vol. 6, no. 1, pp. 19–26, 1980

1980

Earlier work this paper cites.

L. F. Yu, S. K. Yeung, C. K. Tang, D. Terzopoulos, T. F. Chan, and S. J. Osher, “Make it home: automatic optimization of furniture arrangement,” ACM Transactions on Graphics (TOG)-Proceedings of ACM SIGGRAPH 2011, v. 30,(4), July 2011, article no. 86 , vol. 30, no. 4, 2011

2011

Earlier work this paper cites.

P. Vincent, “A connection between score matching and denoising autoencoders,” Neural Computation , vol. 23, no. 7, pp. 1661–1674, 2011

2011

Earlier work this paper cites.

M. J. Schuster, D. Jain, M. Tenorth, and M. Beetz, “Learning organizational principles in human environments,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 3867–3874

2012

Earlier work this paper cites.

M. Fisher, D. Ritchie, M. Savva, T. Funkhouser, and P. Hanrahan, “Example-based synthesis of 3d object arrangements,” ACM Transactions on Graphics (TOG) , vol. 31, no. 6, pp. 1–11, 2012

2012

Earlier work this paper cites.

Y.-T. Yeh, L. Yang, M. Watson, N. D. Goodman, and P. Hanrahan, “Synthesizing open worlds with constraints using locally annealed reversible jump mcmc,” ACM Transactions on Graphics (TOG) , vol. 31, no. 4, pp. 1–11, 2012

2012

Earlier work this paper cites.

N. Abdo, C. Stachniss, L. Spinello, and W. Burgard, “Robot, organize my shelves! tidying up objects by predicting user preferences,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 1557–1564

2015

Earlier work this paper cites.

N. Abdo, C. Stachniss, L. Spinello, and W. Burgard, “Organizing objects by predicting user preferences through collaborative filtering,” The International Journal of Robotics Research , vol. 35, no. 13, pp. 1587–1608, 2016

2016

Earlier work this paper cites.

A. Handa, V. Pătrăucean, S. Stent, and R. Cipolla, “Scenenet: An annotated model generator for indoor scene understanding,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 5737–5743

2016

Earlier work this paper cites.

S. Qi, Y. Zhu, S. Huang, C. Jiang, and S.-C. Zhu, “Human-centric indoor scene synthesis using stochastic grammar,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5899–5908

2018

Earlier work this paper cites.

P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3d point clouds,” in International Conference on Machine Learning . PMLR, 2018, pp. 40–49

2018

Earlier work this paper cites.

X. Zhang, C. Xu, X. Tian, and D. Tao, “Graph edge convolutional neural networks for skeleton-based action recognition,” IEEE transactions on neural networks and learning systems , vol. 31, no. 8, pp. 3047–3060, 2019

2019

Earlier work this paper cites.

D. Batra, A. X. Chang, S. Chernova, A. J. Davison, J. Deng, V. Koltun, S. Levine, J. Malik, I. Mordatch, R. Mottaghi et al. , “Rearrangement: A challenge for embodied ai,” arXiv preprint arXiv:2011.01975 , 2020

Original

2020

Earlier work this paper cites.

Y. Shen, Y. Tay, C. Zheng, D. Bahri, D. Metzler, and A. Courville, “Structformer: Joint unsupervised induction of dependency and constituency structure from masked language modeling,” arXiv preprint arXiv:2012.00857 , 2020

Original

2020

Earlier work this paper cites.

Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” arXiv preprint arXiv:2011.13456 , 2020

Original

2020

Earlier work this paper cites.

I. Kapelyukh and E. Johns, “My house, my rules: Learning tidying preferences with graph neural networks,” in 5th Annual Conference on Robot Learning , 2021

2021

Cited alongside, same era.

Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=PxTIG12RRHS

2021

Cited alongside, same era.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763

2021

Cited alongside, same era.

H. Cao, H.-S. Fang, W. Liu, and C. Lu, “Suctionnet-1billion: A large-scale benchmark for suction grasping,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 8718–8725, 2021

2021

K. Lee, H. Liu, M. Ryu, O. Watkins, Y. Du, C. Boutilier, P. Abbeel, M. Ghavamzadeh, and S. S. Gu, “Aligning text-to-image models using human feedback,” arXiv preprint arXiv:2302.12192 , 2023

Original

2023

Closest in time.

K. Black, M. Janner, Y. Du, I. Kostrikov, and S. Levine, “Training diffusion models with reinforcement learning,” arXiv preprint arXiv:2305.13301 , 2023

Original

2023

Closest in time.

S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu et al. , “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” arXiv preprint arXiv:2303.05499 , 2023

Original

2023

Closest in time.

Q. A. Wei, S. Ding, J. J. Park, R. Sajnani, A. Poulenard, S. Sridhar, and L. Guibas, “Lego-net: Learning regular rearrangements of objects in rooms,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 037–19 047

2023

Closest in time.