Common Sense Initialization of Mixture Density Networks for Motion Planning with Overestimated Number of Components

Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea

Keywords: Mixture Density Networks, Motion Planning, Pretraining, Deep Learning

Abstract:

Mixture density networks (MDNs) are a natural choice to model multi-modal predictions for trajectory prediction or motion planning.
However, MDNs are often difficult to train due to mode collapse and a need for careful initialization, which becomes even more problematic when the number of mixture components are strongly overestimated. To address this issue in motion planning problems, we propose a pre-training scheme for MDNs called common sense initialization (CSI). Pre-training with CSI allows variety-encouraging optimization such as Winner-Takes-All (WTA) to exploit the initialized weights during training so that the MDN can converge when the number of components are overestimated. This paper presents empirical evidence for the effectiveness of CSI when applied to motion planning of pedestrian agents in urban environments.

Submission Number: 75