@inproceedings{icraMainprice2015,
  title = {Predicting Human Reaching Motion in Collaborative Tasks Using Inverse Optimal Control and Iterative Re-planning},
  abstract = {To enable safe and efficient human-robot collaboration
  in shared workspaces, it is important for the robot
  to predict how a human will move when performing a task.
  While predicting human motion for tasks not known a priori is
  very challenging, we argue that single-arm reaching motions for
  known tasks in collaborative settings (which are especially relevant
  for manufacturing) are indeed predictable. Two hypotheses
  underlie our approach for predicting such motions: First, that
  the trajectory the human performs is optimal with respect to an
  unknown cost function, and second, that human adaptation to
  their partner’s motion can be captured well through iterative replanning
  with the above cost function. The key to our approach
  is thus to learn a cost function which “explains” the motion
  of the human. To do this, we gather example trajectories from
  two participants performing a collaborative assembly task using
  motion capture. We then use Inverse Optimal Control to learn
  a cost function from these trajectories. Finally, we predict a
  human’s motion for a given task by iteratively re-planning
  a trajectory for a 23 DoFs human kinematic model using
  the STOMP algorithm with the learned cost function in the
  presence of a moving collaborator. Our results suggest that our
  method outperforms baseline methods and generalizes well for
  tasks similar to those that were demonstrated.},
  year = {2015},
  slug = {icramainprice2015-0fa52c9d-34d2-4691-8c31-30e163f67971},
  author = {Jim Mainprice, Rafi Hayne, Dmitry Berenson}
}