<p> Trajectory planning is a field with widespread utility, and imitation learning pipelines<br>
show promise as an accessible training method for trajectory planning. MPNet is the state<br>
of the art for imitation learning with respect to success rates. MPNet has two general<br>
components to its runtime: a neural network predicts the location of the next anchor point in<br>
a trajectory, and then planning infrastructure applies sampling-based techniques to produce<br>
near-optimal, collision-less paths. This distinction between the two parts of MPNet prompts<br>
investigation into the role of the neural architectures in the Neural Motion Planning pipeline,<br>
to discover where improvements can be made. This thesis seeks to explore the importance<br>
of neural architecture choice by removing the planning structures, and comparing MPNet’s<br>
feedforward anchor point predictor with that of a continuous model trained to output a<br>
continuous trajectory from start to goal. A new state of the art model in continuous learning<br>
is the Neural Flow model. As a continuous model, it possess a low standard deviation runtime<br>
which can be properly leveraged in the absence of planning infrastructure. Neural Flows also<br>
output smooth, continuous trajectory curves that serve to reduce noisy path outputs in the<br>
absence of lazy vertex contraction. This project analyzes the performance of MPNet, Resnet<br>
Flow, and Coupling Flow models when sampling-based planning tools such as dropout, lazy<br>
vertex contraction, and replanning are removed. Each neural planner is trained end-to-end in<br>
an imitation learning pipeline utilizing a simple feedforward encoder, a CNN-based encoder,<br>
and a Pointnet encoder to encode the environment, for purposes of comparison. Results<br>
indicate that performance is competitive, with Neural Flows slightly outperforming MPNet’s<br>
success rates on our reduced dataset in Simple2D, and being slighty outperformed by MPNet<br>
with respect to collision penetration distance in our UR5 Cubby test suite. These results<br>
indicate that continuous models can compete with the performance of anchor point predictor<br>
models when sampling-based planning techniques are not applied. Neural Flow models also<br>
have other benefits that anchor point predictors do not, like continuity guarantees, the ability<br>
to select a proportional location in a trajectory to output, and smoothness. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22681492 |
| Date | 24 April 2023 |
| Creators | Kendal Graham Norman (15344170) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ANALYSIS_OF_CONTINUOUS_LEARNING_MODELS_FOR_TRAJECTORY_REPRESENTATION/22681492 |
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