  Cooperative table-carrying is a complex task due to the continuous nature of
the action and state-spaces, multimodality of strategies, and the need for
instantaneous adaptation to other agents. In this work, we present a method for
predicting realistic motion plans for cooperative human-robot teams on the
task. Using a Variational Recurrent Neural Network (VRNN) to model the
variation in the trajectory of a human-robot team across time, we are able to
capture the distribution over the team's future states while leveraging
information from interaction history. The key to our approach is leveraging
human demonstration data to generate trajectories that synergize well with
humans during test time in a receding horizon fashion. Comparison between a
baseline, sampling-based planner RRT (Rapidly-exploring Random Trees) and the
VRNN planner in centralized planning shows that the VRNN generates motion more
similar to the distribution of human-human demonstrations than the RRT. Results
in a human-in-the-loop user study show that the VRNN planner outperforms
decentralized RRT on task-related metrics, and is significantly more likely to
be perceived as human than the RRT planner. Finally, we demonstrate the VRNN
planner on a real robot paired with a human teleoperating another robot.
