  Natural language provides an intuitive and expressive way of conveying human
intent to robots. Prior works employed end-to-end methods for learning
trajectory deformations from language corrections. However, such methods do not
generalize to new initial trajectories or object configurations. This work
presents ExTraCT, a modular framework for trajectory corrections using natural
language that combines Large Language Models (LLMs) for natural language
understanding and trajectory deformation functions. Given a scene, ExTraCT
generates the trajectory modification features (scene-specific and
scene-independent) and their corresponding natural language textual
descriptions for the objects in the scene online based on a template. We use
LLMs for semantic matching of user utterances to the textual descriptions of
features. Based on the feature matched, a trajectory modification function is
applied to the initial trajectory, allowing generalization to unseen
trajectories and object configurations. Through user studies conducted both in
simulation and with a physical robot arm, we demonstrate that trajectories
deformed using our method were more accurate and were preferred in about 80\%
of cases, outperforming the baseline. We also showcase the versatility of our
system in a manipulation task and an assistive feeding task.
