NavThinker: Action-Conditioned World Models for Coupled Prediction and Planning in Social Navigation

Understand

Social navigation requires robots to act safely in dynamic human environments.

• Effective behavior demands thinking ahead: reasoning about how the scene and pedestrians evolve under different robot actions rather than reacting to current observations alone.
• This creates a coupled prediction-planning challenge, where robot actions and human motion mutually influence each other.
• To address this challenge, we propose NavThinker, a future-aware framework that couples an action-conditioned world model with on-policy reinforcement learning.