arXiv:2602.20055v2 Announce Type: replace-cross Abstract: 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 ov
Advances in large language models (LLMs) and robotic manipulation are converging, enabling more sophisticated and autonomous robotic reasoning in complex, unstructured environments.
This development pushes the boundaries of robot autonomy beyond obstacle avoidance to active environment modification, crucial for practical applications in logistics, disaster response, and domestic services.
Robots equipped with CoReLIN can now dynamically alter their environment to complete tasks, rather than being limited by static paths, significantly expanding their operational capabilities.
- · Robotics companies
- · Logistics and warehousing sectors
- · AI developers
- · Defence and disaster response
- · Companies relying on static automation
- · Traditional low-skill manual labor in some sectors
More capable and adaptable robots will be deployed in complex, unstructured environments.
Increased demand for robust manipulation capabilities and advanced sensor fusion in robotics.
The development of truly general-purpose autonomous robots capable of complex problem-solving in human environments accelerates, impacting labor markets and human-robot interaction.
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Read at arXiv cs.AI