This work proposes a new method for simultaneous probabilistic identification
and control of an observable, fully-actuated mechanical system. Identification
is achieved by conditioning stochastic process priors on observations of
configurations and noisy estimates of configuration derivatives. In contrast to
previous work that has used stochastic processes for identification, we
leverage the
... [Show full abstract] structural knowledge afforded by Lagrangian mechanics and learn
the drift and control input matrix functions of the control-affine system
separately. We utilise feedback-linearisation to reduce, in expectation, the
uncertain nonlinear control problem to one that is easy to regulate in a
desired manner. Thereby, our method combines the flexibility of nonparametric
Bayesian learning with epistemological guarantees on the expected closed-loop
trajectory. We illustrate our method in the context of torque-actuated pendula
where the dynamics are learned with a combination of normal and log-normal
processes.