It is well-known that inverse dynamics models can improve tracking
performance in robot control. These models need to precisely capture the robot
dynamics, which consist of well-understood components, e.g., rigid body
dynamics, and effects that remain challenging to capture, e.g., stick-slip
friction and mechanical flexibilities. Such effects exhibit hysteresis and
partial observability, rendering them, particularly challenging to model.
Hence, hybrid models, which combine a physical prior with data-driven
approaches are especially well-suited in this setting. We present a novel
hybrid model formulation that enables us to identify fully physically
consistent inertial parameters of a rigid body dynamics model which is paired
with a recurrent neural network architecture, allowing us to capture unmodeled
partially observable effects using the network memory. We compare our approach
against state-of-the-art inverse dynamics models on a 7 degree of freedom
manipulator. Using data sets obtained through an optimal experiment design
approach, we study the accuracy of offline torque prediction and generalization
capabilities of joint learning methods. In control experiments on the real
system, we evaluate the model as a feed-forward term for impedance control and
show the feedback gains can be drastically reduced to achieve a given tracking
accuracy.Comment: Accepted for publication at Robotics: Science and System XVIII (RSS),
year 2022. Paper length is 13 pages (i.e. 9 pages of technical content, 1
page of the Bibliography/References and 3 pages of Appendix