The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
DeepMPC: Learning Deep Latent Features for Model Predictive Control
Open accessAbstract
Designing controllers for tasks with complex nonlinear dynamics is extremely challenging, time-consuming, and in many cases, infeasible.This difficulty is exacerbated in tasks such as robotic food-cutting, in which dynamics might vary both with environmental properties, such as material and tool class, and with time while acting.In this work, we present DeepMPC, an online real-time model-predictive control approach designed to handle such difficult tasks.Rather than hand-design a dynamics model for the task, our approach uses a novel deep architecture and learning algorithm, learning controllers for complex tasks directly from data.We validate our method in experiments on a large-scale dataset of 1488 material cuts for 20 diverse classes, and in 450 real-world robotic experiments, demonstrating significant improvement over several other approaches.
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Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
(2010) · Cited by 8,261
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 6,938
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Narendra, K. S., & Annaswamy, A. M. (1989) · Cited by 3,988
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Cited by
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Brunton, S. L., & Kutz, J. N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108380690 · Cited by 1,154
Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?
Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S. N., Rosaen, K., & Vasudevan, R. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989092 · Cited by 622
Deep visual foresight for planning robot motion
Finn, C., & Levine, S. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989324 · Cited by 599
How to train your robot with deep reinforcement learning: Lessons we have learned
Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1805.12114 · Cited by 525
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 441
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452
“Deep neural networks have also been used to learn models for MPC [18].”
MethodAction-Conditional Video Prediction using Deep Networks in Atari Games
Oh, J., Guo, X., Lee, H., Lewis, R. L., & Singh, S. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.08750 · Cited by 419
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., & Finn, C. (2018) · arXiv · doi:10.48550/arxiv.1803.11347 · Cited by 370
Deep learning in robotics: A review of recent research
Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331
Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., & Ryll, M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3246839 · Cited by 231
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Eysenbach, B., Salakhutdinov, R., & Levine, S. (2019) · arXiv · doi:10.48550/arxiv.1906.05253 · Cited by 123
Thananjeyan, B., Balakrishna, A., Rosolia, U., Li, F., McAllister, R., Gonzalez, J. E., Levine, S., Borrelli, F., & Goldberg, K. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976272 · Cited by 83
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F. (2021) · Robotics: Science and Systems XVII · doi:10.15607/rss.2021.xvii.067 · Cited by 66
Young, S., Gandhi, D., Tulsiani, S., Gupta, A., Abbeel, P., & Pinto, L. (2020) · arXiv · doi:10.48550/arxiv.2008.04899 · Cited by 42
Deep multimodal embedding: Manipulating novel objects with point-clouds, language and trajectories
Sung, J., Lenz, I., & Saxena, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989325 · Cited by 26
Self-citationTrajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Seo, Y., Lee, K., Clavera, I., Kurutach, T., Shin, J., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2010.13303 · Cited by 20
Exploration-Driven Generative Interactive Environments
Savov, N., Kazemi, N., Mahdi, M., Paudel, D. P., Wang, X., & Van Gool, L. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.02570 · Cited by 2
“Previous work such as [19, 23, 33] uses light world models to support goal-driven agents with goal-specific state representations.”
Metadata
- Authors
- Lenz, I., Knepper, R., & Saxena, A.
- Venue
- Robotics: Science and Systems XI
- Published
- 2015-07-13
- OpenAlex
- W2281096776
- Cited by
- 332
- References
- 33
- FWCI
- 37.52
- Type
- conference paper
- Language
- en
- Open access
- gold
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