Problem
Research questions and friction points this paper is trying to address.
robotic control
low-level code generation
large language models
real-time feedback
environment-dependent execution
ModuLoop : Low-Level Code Generation using Modular Synthesizer and Closed-Loop Debugger for Robotic Control
🤖 AI Summary
Large language models (LLMs) struggle to directly perform low-level robotic control tasks due to the stringent demands for precision, real-time responsiveness, and environmental awareness. This work proposes a closed-loop, modular code synthesis framework that leverages pretrained LLMs to generate structured control programs. By embedding diagnostic probes into the generated code, the system enables iterative execution–feedback–refinement cycles, producing highly accurate and executable control policies without requiring task-specific fine-tuning. Evaluated on a real-world RGB-D vision and robotic arm platform, the approach achieves high accuracy and strong autonomy in both calibration and pick-and-place tasks, demonstrating the practicality and scalability of the proposed framework.
📝 Abstract
Large Language Models (LLMs) have demonstrated impressive performance across various domains, including code generation and problem solving. However, their application in robotic control, particularly in low-level tasks that require precise manipulation, real-time feedback, and environment-dependent execution, remains limited. To address this challenge, we propose the Closed-Loop Modular Code Synthesizer framework. This framework leverages a pre-trained LLM without any task-specific fine-tuning to perform modular code planning and generation, and iteratively executes the generated code while inserting debugging probes to observe its behavior. This closed-loop structure facilitates systematic debugging and refinement, ultimately producing executable control programs. We apply the proposed framework to the calibration of an RGB-D camera and a robotic arm, validating its effectiveness in real-world settings. Furthermore, through a subsequent pick-and-place task, we demonstrate not only the accuracy of the calibration but also the potential extensibility of the framework. Across both tasks, the framework achieved high execution accuracy and autonomy, illustrating the practicality and scalability of LLM-based robotic control using our framework.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Modular Code Synthesis
Closed-Loop Debugging
LLM-based Robotic Control
Low-Level Code Generation
Autonomous Calibration
🔎 Similar Papers
No similar papers found.
