End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control. (arXiv:2205.13804v1 [cs.RO])

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 …

arxiv dynamics hybrid learning