Kavli Affiliate: Xiang Zhang
| First 5 Authors: Lingfeng Sun, Yixiao Wang, Pin-Yun Hung, Changhao Wang, Xiang Zhang
| Summary:
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. Within this framework, each agent imagines a
virtual cooperative game with others based on its estimation. We demonstrate
this formulation can facilitate the generation of diverse and realistic
interaction patterns in a configurable manner across various scenarios.
Additionally, we have developed a gym-like environment leveraging our
interactive agent model to facilitate the learning and evaluation of
interactive navigation algorithms.
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