In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate our Natural Language Robot Programming (NLRP) framework through simulation and real-world experimentation, using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and a microphone. Participants were asked to complete a pick-and-place task using verbal commands, which were converted into text using Google's Speech-to-Text API and processed through the NLRP framework to obtain joint space trajectories for the robot. Our results indicate that our approach has a high system usability score. The framework's dictionary can be easily extended without relying on transfer learning or large data sets. In the future, we plan to compare the presented framework with different approaches of human-assisted pick-and-place tasks via a comprehensive user study.

翻译：本文提出一种基于语法的自然语言机器人编程框架，专为拾取与放置任务设计。本方法采用自定义动作词词典，将语义相近的词汇归类存储，并可通过从词汇数据库中添加更多动作词轻松扩展词汇量。我们通过仿真与真实环境实验验证了自然语言机器人编程（NLRP）框架的有效性，实验中采用配备校准手部摄像头与麦克风的Franka Panda机械臂。参与者通过语音指令完成拾放任务，指令经Google语音转文本API转换为文本后，由NLRP框架处理生成机器人关节空间轨迹。结果表明，本方法具有较高的系统可用性评分。该框架的词典无需依赖迁移学习或大规模数据集即可轻松扩展。未来，我们计划通过全面的用户研究，将本框架与不同的人机协作拾放任务方法进行对比。