  In this paper, we discuss a framework for teaching bimanual manipulation
tasks by imitation. To this end, we present a system and algorithms for
learning compliant and contact-rich robot behavior from human demonstrations.
The presented system combines insights from admittance control and machine
learning to extract control policies that can (a) recover from and adapt to a
variety of disturbances in time and space, while also (b) effectively
leveraging physical contact with the environment. We demonstrate the
effectiveness of our approach using a real-world insertion task involving
multiple simultaneous contacts between a manipulated object and insertion pegs.
We also investigate efficient means of collecting training data for such
bimanual settings. To this end, we conduct a human-subject study and analyze
the effort and mental demand as reported by the users. Our experiments show
that, while harder to provide, the additional force/torque information
available in teleoperated demonstrations is crucial for phase estimation and
task success. Ultimately, force/torque data substantially improves manipulation
robustness, resulting in a 90% success rate in a multipoint insertion task.
Code and videos can be found at https://bimanualmanipulation.com/
