  Compliant robot behavior is crucial for the realization of contact-rich
manipulation tasks. In such tasks, it is important to ensure a high stiffness
and force tracking accuracy during normal task execution as well as rapid
adaptation and complaint behavior to react to abnormal situations and changes.
In this paper, we propose a novel approach for learning predictive models of
force profiles required for contact-rich tasks. Such models allow detecting
unexpected situations and facilitates better adaptive control. The approach
combines an anomaly detection based on Bidirectional Gated Recurrent Units
(Bi-GRU) and an adaptive force/impedance controller. We evaluated the approach
in simulated and real world experiments on a humanoid robot.The results show
that the approach allow simultaneous high tracking accuracy of desired motions
and force profile as well as the adaptation to force perturbations due to
physical human interaction.
