Low dimensional human preference tracking for motion optimization
Motion planning for high degree of freedom (DOF) robots is not an easy task, and often requires optimization in a high dimensional space. Still, a generic motion planner using a single cost function for optimization may not be optimal over a number of different tasks with various task specific constraints. In this paper, we present a motion planning system that utilizes both easy to communicate human preferences and dimensionality reduction to handle these issues. Joint trajectories with human preference costs are projected into the null space of the task space, which helps make the resulting optimization simpler and more reliable. In addition, we apply the dimensionality reduction for the optimization, which significantly lowers the computational load. The suggested controller has been successfully used in the DARPA Robotics Challenge (DRC) Finals to handle a number of manipulation tasks.