On the Utility of External Agent Trajectory Predictor for Human-AI Coordination

Reaching a consensus on the team plans is vital to human-AI coordination. Although previous studies provide approaches through communication in various ways, it could still be hard to coordinate when the AI has no explainable plan to communicate. To cover this gap, we suggest incorporating external models to assist humans in understanding the intentions of AI agents. In this paper, we propose a two-stage paradigm that first trains an external trajectory prediction model from collected offline trajectories of the target agent and then utilizes the model in human-AI collaboration by displaying the future action predictions of the target agent in real-time. Such a paradigm leaves the AI agent as a black box and thus is available for improving any agents. To test our paradigm, we further implement a transformer-based predictor and develop an online human-AI collaboration platform based on the Overcooked environment for experiments. The comprehensive experimental results verify that human-AI teams can achieve better performance with the help of our model. A user assessment attached to the experiment further demonstrates that our paradigm can significantly enhance the situational awareness of humans. Our study presents the potential to augment the ability of humans via external assistance in human-AI collaboration, which may further inspire future research.