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<!DOCTYPE html>
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<h1 class="title is-1 publication-title"><a style="color:black;">c2g-HOF</a>: <a style="color:black;">Cost-to-Go Function Generating Networks for High Dimensional Motion Planning</a> </h1>
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<a href="https://scholar.google.com/citations?user=qLZYuUMAAAAJ&hl=en" target="_blank">Jinwook<br>Huh</a></span>
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<a href="https://scholar.google.com/citations?user=J0l7wWwAAAAJ&hl=en" target="_blank">Daniel<br>Lee</a></span>
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<a href="https://scholar.google.com/citations?user=Q5KT-hEAAAAJ&hl=en" target="_blank">Volkan<br>Isler</a></span>
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<span class="author-block">Samsung AI Center - New York</span>
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<span>Non-holonomic c2g-HOF arXiv</span>
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This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function. We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.
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System Overview. The dataset contains (1) randomly
generated workspaces, and (2) for each workspace, the cost
to go between pairs of sampled configurations. Training
architecture: C2g generating HOF network encodes an input
workspace and outputs the cost-to-go function represented
as a radial basis function network. Execution: During run
time, following the gradient of the cost-to-go function yields
continuous collision-free trajectories.
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<h2 class="title has-text-centered">c2g-HOF for manipulation</h2>
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<h2 class="title">BibTeX (c2g-HOF)</h2>
<pre><code>@inproceedings{huh2021cost,
title={Cost-to-go function generating networks for high dimensional motion planning},
author={Huh, Jinwook and Isler, Volkan and Lee, Daniel D},
booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages={8480--8486},
year={2021},
organization={IEEE}
}</code></pre>
<h2 class="title">BibTeX (Non-holonomic c2-HOF)</h2>
<pre><code>@inproceedings{huh2021learning,
title={Learning continuous cost-to-go functions for non-holonomic systems},
author={Huh, Jinwook and Lee, Daniel D and Isler, Volkan},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5772--5779},
year={2021},
organization={IEEE}
}</code></pre>
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