  Achieving human-like motion in robots has been a fundamental goal in many
areas of robotics research. Inverse kinematic (IK) solvers have been explored
as a solution to provide kinematic structures with anthropomorphic movements.
In particular, numeric solvers based on geometry, such as FABRIK, have shown
potential for producing human-like motion at a low computational cost.
Nevertheless, these methods have shown limitations when solving for robot
kinematic constraints. This work proposes a framework inspired by FABRIK for
human pose imitation in real-time. The goal is to mitigate the problems of the
original algorithm while retaining the resulting humanlike fluidity and low
cost. We first propose a human constraint model for pose imitation. Then, we
present a pose imitation algorithm (PIC), and it's soft version (PICs) that can
successfully imitate human poses using the proposed constraint system. PIC was
tested on two collaborative robots (Baxter and YuMi). Fifty human
demonstrations were collected for a bi-manual assembly and an incision task.
Then, two performance metrics were obtained for both robots: pose accuracy with
respect to the human and the percentage of environment occlusion/obstruction.
The performance of PIC and PICs was compared against the numerical solver
baseline (FABRIK). The proposed algorithms achieve a higher pose accuracy than
FABRIK for both tasks (25%-FABRIK, 53%-PICs, 58%-PICs). In addition, PIC and
it's soft version achieve a lower percentage of occlusion during incision
(10%-FABRIK, 4%-PICs, 9%-PICs). These results indicate that the PIC method can
reproduce human poses and achieve key desired effects of human imitation.
