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Effective motion planning in high dimensional spaces is a long-standing open problem in robotics.
Real-time obstacle avoidance for manipulators and mobile robots
Khatib, O · 1986
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Potential field methods and their inherent limitations for mobile robot navigation
Koren, Y., Borenstein, J., et al · 1991
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., and Overmars, M. H · 1996
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A wall-following method for escaping local minima in potential field based motion planning
Yun, X. and Tan, K.-C · 1997
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Motion planning in dynamic environments using velocity obstacles
Fiorini, P. and Shiller, Z · 1998
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Robot motion planning and control , volume 229
Laumond, J.-P. et al · 1998
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Rrt-connect: An efficient approach to single-query path planning
Kuffner, J. J. and LaValle, S. M · 2000
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Planning algorithms
LaValle, S. M · 2006
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A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F · 2006
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Crowds by example
Lerner, A., Chrysanthou, Y., and Lischinski, D · 2007
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Reciprocal velocity obstacles for real-time multi-agent navigation
Van den Berg, J., Lin, M., and Manocha, D · 2008
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Chomp: Gradient optimization techniques for efficient motion planning
Ratliff, N., Zucker, M., Bagnell, J. A., and Srinivasa, S · 2009
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Improving data association by joint modeling of pedestrian trajectories and groupings
Pellegrini, S., Ess, A., and Van Gool, L · 2010
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Stomp: Stochastic trajectory optimization for motion planning
Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., and Schaal, S · 2011
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Sampling-based algorithms for optimal motion planning
Karaman, S. and Frazzoli, E · 2011
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Sipp: Safe interval path planning for dynamic environments
Phillips, M. and Likhachev, M · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Sampling-based robot motion planning: A review
Elbanhawi, M. and Simic, M · 2014
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Informed rrt: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
Gammell, J. D., Srinivasa, S. S., and Barfoot, T. D · 2014
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Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
Gammell, J. D., Srinivasa, S. S., and Barfoot, T. D · 2015
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Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions
Janson, L., Schmerling, E., Clark, A., and Pavone, M · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Regionally accelerated batch informed trees (rabit*): A framework to integrate local information into optimal path planning
Choudhury, S., Gammell, J. D., Barfoot, T. D., Srinivasa, S. S., and Scherer, S · 2016
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Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Potential functions based sampling heuristic for optimal path planning
Qureshi, A. H. and Ayaz, Y · 2016
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Safety barrier certificates for collisions-free multirobot systems
Wang, L., Ames, A. D., and Egerstedt, M · 2017
Cited alongside, same era.
Continuous-time gaussian process motion planning via probabilistic inference
Mukadam, M., Dong, J., Yan, X., Dellaert, F., and Boots, B · 2018
Cited alongside, same era.
Ratliff, N. D., Issac, J., Kappler, D., Birchfield, S., and Fox, D · 2018
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Planning with diffusion for flexible behavior synthesis
Janner, M., Du, Y., Tenenbaum, J., and Levine, S · 2022
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Lawson, D. and Qureshi, A. H · 2022
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Compositional visual generation with composable diffusion models
Liu, N., Li, S., Du, Y., Torralba, A., and Tenenbaum, J. B · 2022
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Is conditional generative modeling all you need for decision making?
Ajay, A., Du, Y., Gupta, A., Tenenbaum, J. B., Jaakkola, T. S., and Agrawal, P · 2023
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Rezende, D. J. and Viola, F · 2018
Cited alongside, same era.
Neural path planning: Fixed time, near-optimal path generation via oracle imitation
Bency, M. J., Qureshi, A. H., and Yip, M. C · 2019
Cited alongside, same era.
Implicit generation and modeling with energy based models
Du, Y. and Mordatch, I · 2019
Cited alongside, same era.
Robot motion planning in learned latent spaces
Ichter, B. and Pavone, M · 2019
Cited alongside, same era.
Motion planning networks
Qureshi, A. H., Simeonov, A., Bency, M. J., and Yip, M. C · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Motion planning diffusion: Learning and planning of robot motions with diffusion models
Carvalho, J., Le, A. T., Baierl, M., Koert, D., and Peters, J · 2023
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Denoising heat-inspired diffusion with insulators for collision free motion planning
Chang, J., Ryu, H., Kim, J., Yoo, S., Seo, J., Prakash, N., Choi, J., and Horowitz, R · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Chi, C., Feng, S., Du, Y., Xu, Z., Cousineau, E., Burchfiel, B., and Song, S · 2023
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Imitating task and motion planning with visuomotor transformers
Dalal, M., Mandlekar, A., Garrett, C., Handa, A., Salakhutdinov, R., and Fox, D · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Du, Y., Durkan, C., Strudel, R., Tenenbaum, J. B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W. S · 2023
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Dimsam: Diffusion models as samplers for task and motion planning under partial observability
Fang, X., Garrett, C. R., Eppner, C., Lozano-Pérez, T., Kaelbling, L. P., and Fox, D · 2023
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Motion policy networks
Fishman, A., Murali, A., Eppner, C., Peele, B., Boots, B., and Fox, D · 2023
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Scaling up and distilling down: Language-guided robot skill acquisition
Ha, H., Florence, P., and Song, S · 2023
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Diffusion-based generation, optimization, and planning in 3d scenes
Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., and Zhu, S.-C · 2023
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Efficient diffusion policies for offline reinforcement learning
Kang, B., Ma, X., Du, C., Pang, T., and Yan, S · 2023
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Dall-e-bot: Introducing web-scale diffusion models to robotics
Kapelyukh, I., Vosylius, V., and Johns, E · 2023
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Accelerating motion planning via optimal transport
Le, A. T., Chalvatzaki, G., Biess, A., and Peters, J · 2023
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Hierarchical diffusion for offline decision making
Li, W., Wang, X., Jin, B., and Zha, H · 2023
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Adaptdiffuser: Diffusion models as adaptive self-evolving planners
Liang, Z., Mu, Y., Ding, M., Ni, F., Tomizuka, M., and Luo, P · 2023
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Edmp: Ensemble-of-costs-guided diffusion for motion planning
Saha, K., Mandadi, V., Reddy, J., Srikanth, A., Agarwal, A., Sen, B., Singh, A., and Krishna, M · 2023
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Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Urain, J., Funk, N., Peters, J., and Chalvatzaki, G · 2023
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Diffusion policies as an expressive policy class for offline reinforcement learning
Wang, Z., Hunt, J. J., and Zhou, M · 2023
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Leveraging scene embeddings for gradient-based motion planning in latent space
Yamada, J., Hung, C.-M., Collins, J., Havoutis, I., and Posner, I · 2023
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