The Human Gaze Helps Robots Run Bravely and Efficiently in Crowds.

In human-aware navigation, the robot tacitly games with humans, balancing safety and efficiency according to human intentions. Poor balance or bad intent recognition causes the robot to stop conservatively or advance rashly, resulting in a deadlock or even a collision respectively. To address the issue, this paper proposes an improved limit cycle for collaboratively parameterizing human intentions and planning robot motions. The human-robot interaction is modeled as a dynamic chicken game with incomplete information, where the human gaze is introduced to depict the unique characteristics of each person, allowing the robot to approach with different safety margins. Our method is tested in challenging indoor scenarios and outperforms traditional methods in both safety and efficiency. We enable robots to utilize human wisdom to solve problems that cannot be solved on their own. The robot bravely goes through oncoming crowds by getting closer to people with higher attention on it and has the foresight to stably cross in front or behind people.