Built on

Boggs, P.T., Tolle, J.W.: Sequential quadratic programming. Acta numerica 4
1995
Earlier work this paper cites.
Gill, P.E., Murray, W., Saunders, M.A.: Snopt: An sqp algorithm for large-scale constrained optimization. SIAM review 47
2005
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)
Original
2020
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
Earlier work this paper cites.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International conference on machine learning. pp. 8162–8171. PMLR (2021)
2021
Earlier work this paper cites.

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Then

Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., Song, S.: Diffusion policy: Visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137 (2023)
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Later among the works it cites.
Ketata, M.A., Laue, C., Mammadov, R., Stärk, H., Wu, M., Corso, G., Marquet, C., Barzilay, R., Jaakkola, T.S.: Diffdock-pp: Rigid protein-protein docking with diffusion models. arXiv preprint arXiv:2304.03889 (2023)
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Later among the works it cites.
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Original
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Mazé, F., Ahmed, F.: Diffusion models beat gans on topology optimization. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 9108–9116 (2023)
2023
Later among the works it cites.

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2024

Constraint-Aware Diffusion Models for Trajectory Optimization

Li, Anjian, Ding, Zihan, Dieng, Adji Bousso et al.

Understand

The diffusion model has shown success in generating high-quality and diverse solutions to trajectory optimization problems.

However, diffusion models with neural networks inevitably make prediction errors, which leads to constraint violations such as unmet goals or collisions.
This paper presents a novel constraint-aware diffusion model for trajectory optimization.
We introduce a novel hybrid loss function for training that minimizes the constraint violation of diffusion samples compared to the groundtruth while recovering the original data distribution.

Built on

Boggs, P.T., Tolle, J.W.: Sequential quadratic programming. Acta numerica 4
1995
Earlier work this paper cites.
Gill, P.E., Murray, W., Saunders, M.A.: Snopt: An sqp algorithm for large-scale constrained optimization. SIAM review 47
2005
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)
Original
2020
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
Earlier work this paper cites.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International conference on machine learning. pp. 8162–8171. PMLR (2021)
2021
Earlier work this paper cites.

Similar

Ajay, A., Du, Y., Gupta, A., Tenenbaum, J., Jaakkola, T., Agrawal, P.: Is conditional generative modeling all you need for decision-making? arXiv preprint arXiv:2211.15657 (2022)
Original
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
Original
2022
Cited alongside, same era.
Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., Fleet, D.J.: Video diffusion models. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Hoogeboom, E., Satorras, V.G., Vignac, C., Welling, M.: Equivariant diffusion for molecule generation in 3d. In: International conference on machine learning. pp. 8867–8887. PMLR (2022)
2022
Cited alongside, same era.
Janner, M., Du, Y., Tenenbaum, J.B., Levine, S.: Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991 (2022)

Then

Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., Song, S.: Diffusion policy: Visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137 (2023)
Original
2023
Later among the works it cites.
Ketata, M.A., Laue, C., Mammadov, R., Stärk, H., Wu, M., Corso, G., Marquet, C., Barzilay, R., Jaakkola, T.S.: Diffdock-pp: Rigid protein-protein docking with diffusion models. arXiv preprint arXiv:2304.03889 (2023)
Original
2023
Later among the works it cites.
Krishnamoorthy, S., Mashkaria, S.M., Grover, A.: Diffusion models for black-box optimization. arXiv preprint arXiv:2306.07180 (2023)
Original
2023
Later among the works it cites.
Mazé, F., Ahmed, F.: Diffusion models beat gans on topology optimization. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 9108–9116 (2023)
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2022

Cited alongside, same era.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)

2022

Cited alongside, same era.

Trabucco, B., Geng, X., Kumar, A., Levine, S.: Design-bench: Benchmarks for data-driven offline model-based optimization. In: International Conference on Machine Learning. pp. 21658–21676. PMLR (2022)

2022

Cited alongside, same era.

Botteghi, N., Califano, F., Poel, M., Brune, C.: Trajectory generation, control, and safety with denoising diffusion probabilistic models. arXiv preprint arXiv:2306.15512 (2023)

Original

2023

Cited alongside, same era.

Chang, J., Ryu, H., Kim, J., Yoo, S., Seo, J., Prakash, N., Choi, J., Horowitz, R.: Denoising heat-inspired diffusion with insulators for collision free motion planning. arXiv preprint arXiv:2310.12609 (2023)

Original

2023

Cited alongside, same era.

Power, T., Soltani-Zarrin, R., Iba, S., Berenson, D.: Sampling constrained trajectories using composable diffusion models. In: IROS 2023 Workshop on Differentiable Probabilistic Robotics: Emerging Perspectives on Robot Learning (2023)

2023

Later among the works it cites.

Yang, Z., Mao, J., Du, Y., Wu, J., Tenenbaum, J.B., Lozano-Pérez, T., Kaelbling, L.P.: Compositional diffusion-based continuous constraint solvers. arXiv preprint arXiv:2309.00966 (2023)

Original

2023

Later among the works it cites.

Li, A., Ding, Z., Dieng, A.B., Beeson, R.: Efficient and guaranteed-safe non-convex trajectory optimization with constrained diffusion model. arXiv preprint arXiv:2403.05571 (2024)

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Closest in time.

Sun, Z., Yang, Y.: Difusco: Graph-based diffusion solvers for combinatorial optimization. Advances in Neural Information Processing Systems 36

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Closest in time.