Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.20055v1</id>\n    <title>To Move or Not to Move: Constraint-based Planning Enables Zero-Shot Generalization for Interactive Navigation</title>\n    <updated>2026-02-23T17:10:00Z</updated>\n    <link href='https://arxiv.org/abs/2602.20055v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.20055v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-02-23T17:10:00Z</published>\n    <arxiv:primary_category term='cs.RO'/>\n    <author>\n      <name>Apoorva Vashisth</name>\n      <arxiv:affiliation>Purdue University</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Manav Kulshrestha</name>\n      <arxiv:affiliation>Purdue University</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Pranav Bakshi</name>\n      <arxiv:affiliation>IIT Kharagpur</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Damon Conover</name>\n      <arxiv:affiliation>DEVCOM Army Research Lab</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Guillaume Sartoretti</name>\n      <arxiv:affiliation>National University of Singapore</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Aniket Bera</name>\n      <arxiv:affiliation>Purdue University</arxiv:affiliation>\n    </author>\n  </entry>"
}