  This paper presents Words2Contact, a language-guided multi-contact placement
pipeline leveraging large language models and vision language models. Our
method is a key component for language-assisted teleoperation and human-robot
cooperation, where human operators can instruct the robots where to place their
support contacts before whole-body reaching or manipulation using natural
language. Words2Contact transforms the verbal instructions of a human operator
into contact placement predictions; it also deals with iterative corrections,
until the human is satisfied with the contact location identified in the
robot's field of view. We benchmark state-of-the-art LLMs and VLMs for size and
performance in contact prediction. We demonstrate the effectiveness of the
iterative correction process, showing that users, even naive, quickly learn how
to instruct the system to obtain accurate locations. Finally, we validate
Words2Contact in real-world experiments with the Talos humanoid robot,
instructed by human operators to place support contacts on different locations
and surfaces to avoid falling when reaching for distant objects.
