SocialGAIL: Faithful Crowd Simulation for Social Robot Navigation

Navigation through crowded human environments is challenging for social robots. While reinforcement learning has been adopted for its capacity to capture complex interactions, the training process often relies on simulators to replicate realistic crowd behaviors, ensuring cost-efficiency. Existing crowd simulation methods typically rely on either handcrafted rules, which may lead to overly aggressive navigation, or learning from human trajectory demonstrations, which can be challenging to generalize effectively. In this paper, we introduce a data-driven crowd simulation method called SocialGAIL, which leverages Generative Adversarial Imitation Learning (GAIL) to emulate real pedestrian navigation in crowded environments. SocialGAIL utilizes an attention-based graph neural network to encode observations and employs a generator-discriminator architecture to closely mimic pedestrian behavior. We propose a set of metrics to evaluate the faithfulness of crowd simulation. Experimental results demonstrate that SocialGAIL outperforms baseline methods in terms of goal-reaching, intermediate state faithfulness, trajectory faithfulness, and adherence to global trajectory patterns. The code of our approach is available at https://github.com/William-island/SocialGAIL.