Title | ||
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Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations. |
Abstract | ||
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Soft robots have continuum solid bodies that can deform in an infinite number of ways. Controlling soft robots is very challenging as there are no closed form solutions. We present a learning-in-the-loop co-optimization algorithm in which a latent state representation is learned as the robot figures out how to solve the task. Our solution marries hybrid particle-grid-based simulation with deep, variational convolutional autoencoder architectures that can capture salient features of robot dynamics with high efficacy. We demonstrate our dynamics-aware feature learning algorithm on both 2D and 3D soft robots, and show that it is more robust and faster converging than the dynamics-oblivious baseline. We validate the behavior of our algorithm with visualizations of the learned representation. |
Year | Venue | DocType |
---|---|---|
2019 | NeurIPS | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Andrew Spielberg | 1 | 45 | 6.18 |
Allan Zhao | 2 | 1 | 1.05 |
Yuanming Hu | 3 | 36 | 5.00 |
tao du | 4 | 80 | 5.48 |
Wojciech Matusik | 5 | 4771 | 254.42 |
Daniela Rus | 6 | 7128 | 657.33 |