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Generative models such as diffusion models, excel at capturing high-dimensional distributions with diverse input modalities, e.g.
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M. Toussaint, “Logic-geometric programming: an optimization-based approach to combined task and motion planning,” in IJCAI , 2015
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” NeurIPS , 2017
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B. Kim, L. P. Kaelbling, and T. Lozano-Pérez, “Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience,” in AAAI , 2018
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K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su, “Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding,” in CVPR , 2019
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in NeurIPS , 2020
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C. R. Garrett, C. Paxton, T. Lozano-Pérez, L. P. Kaelbling, and D. Fox, “Online replanning in belief space for partially observable task and motion problems,” in ICRA , 2020
2020
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C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling, “PDDLStream: Integrating Symbolic Planners and Blackbox Samplers,” in ICAPS , 2020
2020
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R. Cai, G. Yang, H. Averbuch-Elor, Z. Hao, S. Belongie, N. Snavely, and B. Hariharan, “Learning gradient fields for shape generation,” in ECCV , 2020
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C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated Task and Motion Planning,” Annual Review of Control, Robotics, and Autonomous Systems , 2021
2021
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Z. Wang, C. R. Garrett, L. P. Kaelbling, and T. Lozano-Pérez, “Learning compositional models of robot skills for task and motion planning,” IJRR , 2021
2021
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A. H. Qureshi, A. Mousavian, C. Paxton, M. C. Yip, and D. Fox, “NeRP: Neural rearrangement planning for unknown objects,” in RSS , 2021
2021
Cited alongside, same era.
D. Xu, A. Mandlekar, R. Martín-Martín, Y. Zhu, S. Savarese, and L. Fei-Fei, “Deep affordance foresight: Planning through what can be done in the future,” in ICRA , 2021
2021
Cited alongside, same era.
M. Janner, Q. Li, and S. Levine, “Offline reinforcement learning as one big sequence modeling problem,” in NeurIPS , 2021
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,” 2021
2021
Cited alongside, same era.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” in NeurIPS , 2021
2021
Cited alongside, same era.
F. Liu, H. Liu, A. Grover, and P. Abbeel, “Masked autoencoding for scalable and generalizable decision making,” in NeurIPS , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Ajay, Y. Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal, “Is conditional generative modeling all you need for decision-making?” in ICLR , 2023
2023
Closest in time.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in RSS , 2023
2023
Closest in time.
U. A. Mishra, S. Xue, Y. Chen, and D. Xu, “Generative skill chaining: Long-horizon skill planning with diffusion models,” in CoRL , 2023
2023
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2021
Cited alongside, same era.
A. Curtis, X. Fang, L. P. Kaelbling, T. Lozano-Pérez, and C. R. Garrett, “Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances,” in ICRA , 2022
2022
Cited alongside, same era.
J. Mao, T. Lozano-Perez, J. B. Tenenbaum, and L. P. Kaelbing, “PDSketch: Integrated Domain Programming, Learning, and Planning,” in NeurIPS , 2022
2022
Cited alongside, same era.
T. Migimatsu, W. Lian, J. Bohg, and S. Schaal, “Symbolic state estimation with predicates for contact-rich manipulation tasks,” in ICRA , 2022
2022
Cited alongside, same era.
T. Silver, A. Athalye, J. B. Tenenbaum, T. Lozano-Pérez, and L. P. Kaelbling, “Learning neuro-symbolic skills for bilevel planning,” in CoRL , 2022
2022
Cited alongside, same era.
D. Driess, J.-S. Ha, M. Toussaint, and R. Tedrake, “Learning models as functionals of signed-distance fields for manipulation planning,” in CoRL , 2022
2022
Cited alongside, same era.
M. Janner, Y. Du, J. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” in ICML , 2022
2022
Cited alongside, same era.
Closest in time.
J. Carvalho, A. T. Le, M. Baierl, D. Koert, and J. Peters, “Motion planning diffusion: Learning and planning of robot motions with diffusion models,” in IROS , 2023
2023
Closest in time.
Z. Yang, J. Mao, Y. Du, J. Wu, J. B. Tenenbaum, T. Lozano-Pérez, and L. P. Kaelbling, “Compositional Diffusion-Based Continuous Constraint Solvers,” in CoRL , 2023
2023
Closest in time.
J. Mendez-Mendez, L. P. Kaelbling, and T. Lozano-Pérez, “Embodied lifelong learning for task and motion planning,” in CoRL , 2023
2023
Closest in time.
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.
A. Simeonov, A. Goyal, L. Manuelli, L. Yen-Chen, A. Sarmiento, A. Rodriguez, P. Agrawal, and D. Fox, “Shelving, stacking, hanging: Relational pose diffusion for multi-modal rearrangement,” in CoRL , 2023
2023
Closest in time.
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 ICRA , 2023
2023
Closest in time.