To Move or Not to Move: Constraint-based Planning Enables Zero-Shot Generalization for Interactive Navigation

TL;DR

This paper introduces a constraint-based planning framework driven by large language models for interactive navigation, enabling robots to manipulate clutter and adapt to complex environments for object placement tasks.

Contribution

It presents a novel LLM-driven, constraint-based planning approach with active perception for lifelong interactive navigation in cluttered environments.

Findings

Outperforms baseline methods in simulation

Enables zero-shot generalization to new environments

Demonstrates effective real-world hardware deployment

Abstract

Visual navigation typically assumes the existence of at least one obstacle-free path between start and goal, which must be discovered/planned by the robot. However, in real-world scenarios, such as home environments and warehouses, clutter can block all routes. Targeted at such cases, we introduce the Lifelong Interactive Navigation problem, where a mobile robot with manipulation abilities can move clutter to forge its own path to complete sequential object- placement tasks - each involving placing an given object (eg. Alarm clock, Pillow) onto a target object (eg. Dining table, Desk, Bed). To address this lifelong setting - where effects of environment changes accumulate and have long-term effects - we propose an LLM-driven, constraint-based planning framework with active perception. Our framework allows the LLM to reason over a structured scene graph of discovered objects and obstacles, deciding which object to move, where to place it, and where to look next to discover task-relevant information. This coupling of reasoning and active perception allows the agent to explore the regions expected to contribute to task completion rather than exhaustively mapping the environment. A standard motion planner then executes the corresponding navigate-pick-place, or detour sequence, ensuring reliable low-level control. Evaluated in physics-enabled ProcTHOR-10k simulator, our approach outperforms non-learning and learning-based baselines. We further demonstrate our approach qualitatively on real-world hardware.