  Robotic assembly tasks involve complex and low-clearance insertion
trajectories with varying contact forces at different stages. While the nominal
motion trajectory can be easily obtained from human demonstrations through
kinesthetic teaching, teleoperation, simulation, among other methods, the force
profile is harder to obtain especially when a real robot is unavailable. It is
difficult to obtain a realistic force profile in simulation even with physics
engines. Such simulated force profiles tend to be unsuitable for the actual
robotic assembly due to the reality gap and uncertainty in the assembly
process. To address this problem, we present a combined learning-based
framework to imitate human assembly skills through hybrid trajectory learning
and force learning. The main contribution of this work is the development of a
framework that combines hierarchical imitation learning, to learn the nominal
motion trajectory, with a reinforcement learning-based force control scheme to
learn an optimal force control policy, that can satisfy the nominal trajectory
while adapting to the force requirement of the assembly task. To further
improve the imitation learning part, we develop a hierarchical architecture,
following the idea of goal-conditioned imitation learning, to generate the
trajectory learning policy on the \textit{skill} level offline. Through
experimental validations, we corroborate that the proposed learning-based
framework is robust to uncertainty in the assembly task, can generate
high-quality trajectories, and can find suitable force control policies, which
adapt to the task's force requirements more efficiently.
