CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

Robot navigation typically assumes an obstacle-free path exists between start and goal.

In real environments, however, clutter may block all routes.

We introduce Lifelong Interactive Navigation, where a mobile robot with manipulation capabilities must move objects to forge paths and complete sequential object-placement tasks.

Because environment modifications persist, decisions impact future navigability and task difficulty.

We propose CoReLIN, an LLM-driven constraint-based reasoning framework with active perception.

CoReLIN reasons over a structured scene graph to decide which objects to relocate, where to place them, and where to explore next.

A standard motion planner executes reliable navigation and manipulation primitives.

To evaluate long-horizon behavior, we introduce 2 new metrics - Long-term Efficiency Score (LES), a unified metric capturing success, execution efficiency, environment optimality, captured by Price of Clutter.

In ProcTHOR-10k, CoReLIN outperforms best baseline by 16% under standard metrics and LES, and transfers to real-world hardware.