Bridging Intent and Assembly with MLLMs

Brick-Composer addresses the challenge of autonomous assembly in environments where components are diverse and non-standardized. Traditional robotic assembly often relies on rigid, pre-programmed paths or specific CAD-based constraints. Brick-Composer shifts this paradigm by utilizing Multimodal Large Language Models (MLLMs) to interpret high-level assembly goals and translate them into actionable sequences for handling heterogeneous parts.

The Mechanism of Compositional Reasoning

The core innovation lies in the model's ability to perform compositional reasoning. By processing visual inputs of available parts alongside textual assembly instructions, the system identifies spatial relationships and structural dependencies. This allows the agent to determine not just what to assemble, but the logical order of operations required to maintain structural integrity throughout the build process. The system effectively treats assembly as a multi-step planning problem where the MLLM acts as the central controller, evaluating the state of the workspace after each placement to adjust for potential errors or misalignments.

Practical Implications for Robotic Manipulation

By moving away from hard-coded assembly logic, Brick-Composer demonstrates increased flexibility in handling novel or irregular components. The approach suggests that MLLMs can serve as a robust interface for robotic systems, enabling them to interpret natural language instructions for complex physical tasks. This reduces the need for extensive retraining when the set of available components changes, as the model relies on its generalized understanding of spatial geometry and structural logic rather than specific, pre-defined part libraries.