IDfRA: Self-Verification for Iterative Design in Robotic Assembly

Abstract

Design for Robotic Assembly (DfRA) remains largely dependent on manual planning and heuristic simulation, limiting scalability and robustness in complex industrial settings. Although large language models (LLMs) show promise for semantic reasoning and task planning, most approaches remain tightly coupled to pre-built simulators that assume an accurate world model. We introduce Iterative Design for Robotic Assembly (IDfRA), a closed-loop framework that combines an LLM for plan generation with a vision–language model (VLM) for execution assessment. Given a target structure and a partial environmental signature, the LLM proposes an assembly plan, the robot executes it once at test time, and the VLM evaluates the resulting state to provide feedback for replanning. Through this iterative planning–execution–verification loop, the system progressively improves semantic fidelity and physical feasibility. Crucially, IDfRA does not require an accurate a priori world model before deployment. Instead, physical constraints are discovered online through interaction, enabling adaptation to under-specified environments. Empirical evaluation demonstrates that IDfRA attains 73.3% top-1 accuracy in semantic recognisability, surpassing the baseline on this metric. Moreover, the resulting assembly plans exhibit robust physical feasibility, achieving an overall 86.9% construction success rate, with design quality improving across iterations, albeit not always monotonically. Pairwise human evaluation further corroborates the advantages of IDfRA relative to alternative approaches. By integrating self-verification with context-aware adaptation, the framework evidences strong potential for deployment in unstructured manufacturing scenarios.