  In this work, we present an approach to identify sub-tasks within a
demonstrated robot trajectory using language instructions. We identify these
sub-tasks using language provided during demonstrations as guidance to identify
sub-segments of a longer robot trajectory. Given a sequence of natural language
instructions and a long trajectory consisting of image frames and discrete
actions, we want to map an instruction to a smaller fragment of the trajectory.
Unlike previous instruction following works which directly learn the mapping
from language to a policy, we propose a language-conditioned change-point
detection method to identify sub-tasks in a problem. Our approach learns the
relationship between constituent segments of a long language command and
corresponding constituent segments of a trajectory. These constituent
trajectory segments can be used to learn subtasks or sub-goals for planning or
options as demonstrated by previous related work. Our insight in this work is
that the language-conditioned robot change-point detection problem is similar
to the existing video moment retrieval works used to identify sub-segments
within online videos. Through extensive experimentation, we demonstrate a
$1.78_{\pm 0.82}\%$ improvement over a baseline approach in accurately
identifying sub-tasks within a trajectory using our proposed method. Moreover,
we present a comprehensive study investigating sample complexity requirements
on learning this mapping, between language and trajectory sub-segments, to
understand if the video retrieval-based methods are realistic in real robot
scenarios.
