Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors

Understand

Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces.

• Unlike static or predictably moving obstacles, human behavior is inherently complex and unpredictable, stemming from dynamic interactions with other agents.
• Existing simulation tools frequently fail to adequately model such reactive and collaborative behaviors, impeding the development and evaluation of robust social navigation strategies.
• This paper introduces a novel framework utilizing distributed potential games to simulate human-like interactions in highly interactive scenarios.