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LLMs for Coding and Robotics Education

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arxiv 2402.06116 v1 pith:QDQ2RLYE submitted 2024-02-09 cs.RO cs.AI

LLMs for Coding and Robotics Education

classification cs.RO cs.AI
keywords modelsrobotcodinglanguagelargecodeeducationblock
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced technologies. Within this context, robot coding education is garnering increasing attention. To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification. In this paper, we highlight an important trend in robot coding education. We test several mainstream large language models on both traditional coding tasks and the more challenging task of robot code generation, which includes block diagrams. Our results show that GPT-4V outperforms other models in all of our tests but struggles with generating block diagram images.

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Cited by 2 Pith papers

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  2. Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards

    cs.LG 2026-05 unverdicted novelty 5.0

    A PPO-based RL framework with execution-aware dense rewards and token-level mapping improves pass@1 by 19% on MBPP and reduces execution failures by 51% on RoboEval for LLM code generation.