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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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?
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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
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
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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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