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Motion planning for autonomous robots in tight, interaction-rich, and mixedhuman-robot environments is challenging. State-of-the-art methods typicallyseparate prediction and planning, predicting other agents' trajectories firstand then planning the ego agent's motion in the remaining free space. However,agents' lack of awareness of their influence on others can lead to the freezingrobot problem. We build upon Interaction-Aware Model Predictive Path Integral(IA-MPPI) control and combine it with learning-based trajectory predictions,thereby relaxing its reliance on communicated short-term goals for otheragents. We apply this framework to Autonomous Surface Vessels (ASVs) navigatingurban canals. By generating an artificial dataset in real sections ofAmsterdam's canals, adapting and training a prediction model for our domain,and proposing heuristics to extract local goals, we enable effectivecooperation in planning. Our approach improves autonomous robot navigation incomplex, crowded environments, with potential implications for multi-agentsystems and human-robot interaction.

Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions