  One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in
handling the uncertainty in the location of the target hole. In order to
address it, high-dimensional sensor inputs from sensor modalities such as
vision, force/torque sensing, and proprioception can be combined to learn
control policies that are robust to this uncertainty in the target pose.
Whereas deep learning has shown success in recognizing objects and making
decisions with high-dimensional inputs, the learning procedure might damage the
robot when applying directly trial- and-error algorithms on the real system. At
the same time, learning from Demonstration (LfD) methods have been shown to
achieve compelling performance in real robotic systems by leveraging
demonstration data provided by experts. In this paper, we investigate the
merits of multiple sensor modalities such as vision, force/torque sensors, and
proprioception when combined to learn a controller for real world assembly
operation tasks using LfD techniques. The study is limited to PiH insertions;
we plan to extend the study to more experiments in the future. Additionally, we
propose a multi-step-ahead loss function to improve the performance of the
behavioral cloning method. Experimental results on a real manipulator support
our findings, and show the effectiveness of the proposed loss function.
