DeepSocNav: Social Navigation by Imitating Human Behaviors
This work addresses social navigation for robots or agents by providing a method to create training data, but it is incremental as it builds on existing datasets and techniques.
The paper tackled the problem of social navigation by generating synthetic first-person depth data from existing bird's-eye view datasets using game engines, and proposed DeepSocNav, a model that outperformed baselines in social navigation scores.
Current datasets to train social behaviors are usually borrowed from surveillance applications that capture visual data from a bird's-eye perspective. This leaves aside precious relationships and visual cues that could be captured through a first-person view of a scene. In this work, we propose a strategy to exploit the power of current game engines, such as Unity, to transform pre-existing bird's-eye view datasets into a first-person view, in particular, a depth view. Using this strategy, we are able to generate large volumes of synthetic data that can be used to pre-train a social navigation model. To test our ideas, we present DeepSocNav, a deep learning based model that takes advantage of the proposed approach to generate synthetic data. Furthermore, DeepSocNav includes a self-supervised strategy that is included as an auxiliary task. This consists of predicting the next depth frame that the agent will face. Our experiments show the benefits of the proposed model that is able to outperform relevant baselines in terms of social navigation scores.