Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification

We present an efficient algorithm for motion planning and controlling a robot system with a high number of degrees-of-freedom (DOF). These systems include high-DOF soft robots and articulated robots interacting with a deformable environment. We present a novel technique to accelerate the evaluations of the forward dynamics function by storing the results of costly computations in a hierarchical adaptive grid. Furthermore, we exploit the underactuated properties of the robot systems and build the grid in a low-dimensional space. Our approach approximates the forward dynamics function with guaranteed error bounds and can be used in optimization-based motion planning and reinforcement-learning-based feed-back control. We highlight the performance on two high-DOF robot systems: a line-actuated elastic robot arm and an underwater swimming robot in water. Compared to prior techniques based on exact dynamics evaluation, we observe one to two orders of magnitude improvement in the performance.