We present a discrete-optimization technique for finding feasible robot arm trajectories that pass through provided 6-DOF Cartesian-space end-effector paths with high accuracy, a problem called pathwise-inverse kinematics. The output from our method consists of a path function of joint-angles that best follows the provided end-effector path function, given some definition of ``best''. Our method, called Stampede, casts the robot motion translation problem as a discrete-space graph-search problem where the nodes in the graph are individually solved for using non-linear optimization; framing the problem in such a way gives rise to a well-structured graph that affords an effective best path calculation using an efficient dynamic-programming algorithm. We present techniques for sampling configuration space, such as diversity sampling and adaptive sampling, to construct the search-space in the graph. Through an evaluation, we show that our approach performs well in finding smooth, feasible, collision-free robot motions that match the input end-effector trace with very high accuracy, while alternative approaches, such as a state-of-the-art per-frame inverse kinematics solver and a global non-linear trajectory-optimization approach, performed unfavorably.

@InProceedings{RMG19,
  author       = "Rakita, Daniel and Mutlu, Bilge and Gleicher, Michael",
  title        = "Stampede: A Discrete-Optimization Method for Solving Pathwise-Inverse Kinematics",
  booktitle    = "2019 International Conference on Robotics and Automation (ICRA)",
  month        = "may",
  year         = "2019",
  ee           = "https://ieeexplore.ieee.org/document/8793617",
  doi          = "10.1109/ICRA.2019.8793617",
  url          = "http://graphics.cs.wisc.edu/Papers/2019/RMG19"
}