  Collaboration between human and robot requires effective modes of
communication to assign robot tasks and coordinate activities. As communication
can utilize different modalities, a multi-modal approach can be more expressive
than single modal models alone. In this work we propose a co-speech gesture
model that can assign robot tasks for human-robot collaboration. Human gestures
and speech, detected by computer vision and speech recognition, can thus refer
to objects in the scene and apply robot actions to them. We present an
experimental evaluation of the multi-modal co-speech model with a real-world
industrial use case. Results demonstrate that multi-modal communication is easy
to achieve and can provide benefits for collaboration with respect to single
modal tools.
