  To realize human-robot collaboration, robots need to execute actions for new
tasks according to human instructions given finite prior knowledge. Human
experts can share their knowledge of how to perform a task with a robot through
multi-modal instructions in their demonstrations, showing a sequence of
short-horizon steps to achieve a long-horizon goal. This paper introduces a
method for robot action sequence generation from instruction videos using (1)
an audio-visual Transformer that converts audio-visual features and instruction
speech to a sequence of robot actions called dynamic movement primitives (DMPs)
and (2) style-transfer-based training that employs multi-task learning with
video captioning and weakly-supervised learning with a semantic classifier to
exploit unpaired video-action data. We built a system that accomplishes various
cooking actions, where an arm robot executes a DMP sequence acquired from a
cooking video using the audio-visual Transformer. Experiments with
Epic-Kitchen-100, YouCookII, QuerYD, and in-house instruction video datasets
show that the proposed method improves the quality of DMP sequences by 2.3
times the METEOR score obtained with a baseline video-to-action Transformer.
The model achieved 32% of the task success rate with the task knowledge of the
object.
