  Modelling robot dynamics accurately is essential for control, motion
optimisation and safe human-robot collaboration. Given the complexity of modern
robotic systems, dynamics modelling remains non-trivial, mostly in the presence
of compliant actuators, mechanical inaccuracies, friction and sensor noise.
Recent efforts have focused on utilising data-driven methods such as Gaussian
processes and neural networks to overcome these challenges, as they are capable
of capturing these dynamics without requiring extensive knowledge beforehand.
While Gaussian processes have shown to be an effective method for learning
robotic dynamics with the ability to also represent the uncertainty in the
learned model through its variance, they come at a cost of cubic time
complexity rather than linear, as is the case for deep neural networks. In this
work, we leverage the use of deep kernel models, which combine the
computational efficiency of deep learning with the non-parametric flexibility
of kernel methods (Gaussian processes), with the overarching goal of realising
an accurate probabilistic framework for uncertainty quantification. Through
using the predicted variance, we adapt the feedback gains as more accurate
models are learned, leading to low-gain control without compromising tracking
accuracy. Using simulated and real data recorded from a seven degree-of-freedom
robotic manipulator, we illustrate how using stochastic variational inference
with deep kernel models increases compliance in the computed torque controller,
and retains tracking accuracy. We empirically show how our model outperforms
current state-of-the-art methods with prediction uncertainty for online inverse
dynamics model learning, and solidify its adaptation and generalisation
capabilities across different setups.
