Gated Path Planning Networks
Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan, Salakhutdinov

TL;DR
This paper introduces the Gated Path Planning Network, an improved differentiable navigation module that addresses optimization issues in Value Iteration Networks by incorporating gating mechanisms, leading to better performance and generalization.
Contribution
It reframes VINs as gated recurrent convolutional networks, proposing a new architecture that improves training stability and generalization in path planning tasks.
Findings
Outperforms VIN in learning speed and stability
Shows robustness across maze types and sizes
Succeeds in a 3D environment with RGB inputs
Abstract
Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Multimodal Machine Learning Applications · Human Pose and Action Recognition
