SMOOTH OPERATOR: IN-PLACE SYMBOLIC PLAN REPAIR VIA CONTINUAL OPEN-WORLD PREDICATE AND OPERATOR INVENTION

Open-world embodied agents frequently fail during execution because predefined symbolic planning domains are fundamentally incomplete. Existing recovery methods address unmet preconditions or planning impasses by re-planning globally from scratch, causing severe plan churn. In contrast, we tackle failures where nominal preconditions are met, yet physical execution stalls and the intended action effects are not observed. We propose SMOOTH (Symbolic Model Open-World Online Repair through Tested Hypotheses), a neuro-symbolic framework for online, in-place plan repair and continual domain induction. Upon failure, SMOOTH leverages a vision-language model to jointly induce open-vocabulary relational predicates and synthesize novel operators composed from primitive skills. Candidate abstractions are physically verified via counterfactual simulation rehearsals to avoid un-grounded proposals. Verified compensatory actions are spliced directly at the failure point, preserving the valid downstream plan suffix and minimizing plan disruption. Discovered abstractions are consolidated into persistent memory for lifelong reuse. Evaluations on OpenRepairBench and Mini-BEHAVIOR show that SMOOTH achieves higher task success and domain reuse than state-of-the-art baselines, while our ablations highlight the critical roles of in-place path preservation and physical validation.