Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning

ArXi:2506.15828v3 Announce Type: replace-cross Embodied agents need to plan and act reliably in real and complex 3D environments. Classical planning (e.g., PDDL) offers structure and guarantees, but in practice it fails under noisy perception and incorrect predicate grounding. On the other hand, Large Language Models (LLMs)-based planners leverage commonsense reasoning, yet frequently propose actions that are unfeasible or unsafe. Following recent works that combine the two approaches, we