Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

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 replanning a trajectory for a 23 DoF 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.