  Motion planning for autonomous robots in tight, interaction-rich, and mixed
human-robot environments is challenging. State-of-the-art methods typically
separate prediction and planning, predicting other agents' trajectories first
and 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 freezing
robot 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 other
agents. We apply this framework to Autonomous Surface Vessels (ASVs) navigating
urban canals. By generating an artificial dataset in real sections of
Amsterdam's canals, adapting and training a prediction model for our domain,
and proposing heuristics to extract local goals, we enable effective
cooperation in planning. Our approach improves autonomous robot navigation in
complex, crowded environments, with potential implications for multi-agent
systems and human-robot interaction.
