  Delicate industrial insertion tasks (e.g., PC board assembly) remain
challenging for industrial robots. The challenges include low error tolerance,
delicacy of the components, and large task variations with respect to the
components to be inserted. To deliver a feasible robotic solution for these
insertion tasks, we also need to account for hardware limits of existing
robotic systems and minimize the integration effort. This paper proposes a
composable framework for efficient integration of a safe insertion policy on
existing robotic platforms to accomplish these insertion tasks. The policy has
an interpretable modularized design and can be learned efficiently on hardware
and transferred to new tasks easily. In particular, the policy includes a safe
insertion agent as a baseline policy for insertion, an optimal configurable
Cartesian tracker as an interface to robot hardware, a probabilistic inference
module to handle component variety and insertion errors, and a safe learning
module to optimize the parameters in the aforementioned modules to achieve the
best performance on designated hardware. The experiment results on a UR10 robot
show that the proposed framework achieves safety (for the delicacy of
components), accuracy (for low tolerance), robustness (against perception error
and component defection), adaptability and transferability (for task
variations), as well as task efficiency during execution plus data and time
efficiency during learning.
