  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.
