  In collaborative human-robot manipulation, a robot must predict human intents
and adapt its actions accordingly to smoothly execute tasks. However, the
human's intent in turn depends on actions the robot takes, creating a
chicken-or-egg problem. Prior methods ignore such inter-dependency and instead
train marginal intent prediction models independent of robot actions. This is
because training conditional models is hard given a lack of paired human-robot
interaction datasets. Can we instead leverage large-scale human-human
interaction data that is more easily accessible? Our key insight is to exploit
a correspondence between human and robot actions that enables transfer learning
from human-human to human-robot data. We propose a novel architecture,
InteRACT, that pre-trains a conditional intent prediction model on large
human-human datasets and fine-tunes on a small human-robot dataset. We evaluate
on a set of real-world collaborative human-robot manipulation tasks and show
that our conditional model improves over various marginal baselines. We also
introduce new techniques to tele-operate a 7-DoF robot arm and collect a
diverse range of human-robot collaborative manipulation data, which we
open-source.
