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Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming

arXiv 2312.13905 ↗First posted 21 Dec 2023cs.RO cs.AI cs.CLcited by 1

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Abstract (arXiv)

Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 9 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

4 of 9 references.

References · 4

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  4. 2023Large Language Models as Zero-Shot Human Models for Human-Robot Interactioncited by 30

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  1. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 16