Learning Strategies For Successful Crowd Navigation
This work addresses the challenge of enabling robots to navigate safely and efficiently in dynamic human environments, though it appears incremental as it builds on existing neural network methods for crowd navigation.
The paper tackles the problem of autonomous robot navigation in human crowds by using a neural network to learn context-specific strategies in-situ, achieving quantitative improvements in navigation performance.
Teaching autonomous mobile robots to successfully navigate human crowds is a challenging task. Not only does it require planning, but it requires maintaining social norms which may differ from one context to another. Here we focus on crowd navigation, using a neural network to learn specific strategies in-situ with a robot. This allows us to take into account human behavior and reactions toward a real robot as well as learn strategies that are specific to various scenarios in that context. A CNN takes a top-down image of the scene as input and outputs the next action for the robot to take in terms of speed and angle. Here we present the method, experimental results, and quantitatively evaluate our approach.