  The ability to learn from human demonstration endows robots with the ability
to automate various tasks. However, directly learning from human demonstration
is challenging since the structure of the human hand can be very different from
the desired robot gripper. In this work, we show that manipulation skills can
be transferred from a human to a robot through the use of micro-evolutionary
reinforcement learning, where a five-finger human dexterous hand robot
gradually evolves into a commercial robot, while repeated interacting in a
physics simulator to continuously update the policy that is first learned from
human demonstration. To deal with the high dimensions of robot parameters, we
propose an algorithm for multi-dimensional evolution path searching that allows
joint optimization of both the robot evolution path and the policy. Through
experiments on human object manipulation datasets, we show that our framework
can efficiently transfer the expert human agent policy trained from human
demonstrations in diverse modalities to target commercial robots.
