Manipulation in partially observable environments requires planning under incomplete scene information. In such settings, an initially valid plan may execute successfully yet remain insufficient for task completion. Existing foundation-model-guided ta
Manipulation in partially observable environments requires planning under incomplete scene information. In such settings, an initially valid plan may execute successfully yet remain insufficient for task completion. Existing foundation-model-guided task and motion planning (TAMP) systems can generate useful long-horizon task decompositions, subgoals, or constraints, but they often assume having access to a fully specified scene state or invoke model-level replanning after a subgoal, refinement, or execution attempt fails. We present ROBUST TAMP, a modular LLM/VLM-guided planning framework for reactive TAMP where unseen task-relevant and non-target objects may become visible during execution. The framework restricts the foundation-model planner to the currently visible relational scene state, validates generated task-level actions against a strict executable interface, and routes the accepted actions to scene-specific execution adapters. Object discovery is treated as a distinct replanning event and, after a stable execution horizon, the system reconstructs the visible scene state and replans using completed-action history and structured replanning event context. Evaluations are performed on six RLBench/CoppeliaSim kitchen and grill variants involving hidden objects, non-target object discovery, articulated-container interaction, and temporal manipulation procedures. We compare text-only LLM and VLM planners of different sizes under the same validation, execution, monitoring, and replanning pipeline, reporting task success, partial goal completion, discovery- and failure-triggered replanning behavior, implicit non-target-object handling, and planner inference cost.