Daniel Rakita is a Ph.D. student of computer science at the University of Wisconsin-Madison advised by Michael Gleicher and Bilge Mutlu. He received a Bachelors of Music Performance from the Indiana University Jacobs School of Music in 2012. His research lies at the intersection of motion planning, trajectory optimization, shared-control, and human-robot interaction, and commonly involves creating motion optimization approaches that allow robot manipulators to move smoothly and accurately in real-time.
In order to complete tasks, robots need to consistently calculate joint-space trajectories such that their end-effectors pass through the correct position, with the correct orientation, at the correct time. In many cases, these trajectories must not only be accurate, i.e., avoiding large end-effector translation or orientation errors; and feasible, i.e., avoiding self-collisions, joint-space discontinuities, or kinematic singularities; but they must also be calculated quickly to afford robots the ability to act and react in uncertain or dynamic environments. In this talk, I will overview technical methods we have developed that attempt to achieve such feasible, accurate, and time-sensitive robot-arm motions. In particular, I will detail our inverse kinematics solver called RelaxedIK that utilizes both non-linear optimization and machine learning to achieve a smooth, feasible, and accurate end-effector to joint-space mapping on-the-fly. I will highlight numerous ways we have applied our technical methods to real-world-inspired problems, such as mapping human-arm-motion to robot-arm-motion in real-time to afford effective shared-control interfaces and automatically moving a camera-in-hand robot in a remote setting to optimize a viewpoint for a teleoperator. I will conclude with a preview of our ongoing and future work in this space.
Rakita, Daniel, Bilge Mutlu, and Michael Gleicher. “An autonomous dynamic camera method for effective remote teleoperation.” Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. 2018. link - video