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In this paper, we present a grammar-based natural language framework forrobot programming, specifically for pick-and-place tasks. Our approach uses acustom dictionary of action words, designed to store together words that sharemeaning, allowing for easy expansion of the vocabulary by adding more actionwords from a lexical database. We validate our Natural Language RobotProgramming (NLRP) framework through simulation and real-world experimentation,using a Franka Panda robotic arm equipped with a calibrated camera-in-hand anda microphone. Participants were asked to complete a pick-and-place task usingverbal commands, which were converted into text using Google's Speech-to-TextAPI and processed through the NLRP framework to obtain joint space trajectoriesfor the robot. Our results indicate that our approach has a high systemusability score. The framework's dictionary can be easily extended withoutrelying on transfer learning or large data sets. In the future, we plan tocompare the presented framework with different approaches of human-assistedpick-and-place tasks via a comprehensive user study.

Natural Language Robot Programming: NLP integrated with autonomous robotic grasping