Middle Code Prediction: Enhancing Code Generation for Uncommon Programming Languages in Robotics

Zixi Jia, Hongbin Gao, Hexiao Li

Keywords: Code generation, Large Language models, Robot, Prompt learning

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TL;DR: This paper addresses the issue of code generation for uncommon programming languages that typically rely on high-level API encapsulation by proposing a three-phase approach called Middle Code Prediction (MCP)

Abstract: Generating executable code through natural language instructions to drive robotic movements is considered a crucial step towards achieving embodied intelligence. However, in the robotics domain, the scarcity of programming language data often necessitates manually encapsulating high-level APIs to enable Large Language Models(LLMs) to predict code correctly, which is time-consuming and incomplete. Therefore, this paper proposes a three-stage Middle Code Prediction(MCP) scheme, by injecting appropriate prompts at different stages, the LLMs can shift towards predicting middle code that it understands more easily. This middle code can then be converted into the final code through specific scripts, accomplishing the task of generating code in uncommon programming languages automatically and without the need for manually encapsulating high-level APIs. We tested our approach on Hospital Item Transport Dataset(HITD) and found that MCP could improve the mean accuracy of various baseline models to varying degrees, with an overall increase of 31%, while also enhancing the noise resistance of fine-tuned models. We conducted real-world experiments on industrial robotic arms, verifying the feasibility of MCP in scenarios with no API and partial API encapsulation. The method proposed in this paper provides a guideline for code generation in uncommon programming languages within the context of LLMs. Our experimental dataset is available at https://github.com/Ghbbbbb/MCP.

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

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Submission Number: 332