  Some imitation learning methods combine behavioural cloning with
self-supervision to infer actions from state pairs. However, most rely on a
large number of expert trajectories to increase generalisation and human
intervention to capture key aspects of the problem, such as domain constraints.
In this paper, we propose Continuous Imitation Learning from Observation
(CILO), a new method augmenting imitation learning with two important features:
(i) exploration, allowing for more diverse state transitions, requiring less
expert trajectories and resulting in fewer training iterations; and (ii) path
signatures, allowing for automatic encoding of constraints, through the
creation of non-parametric representations of agents and expert trajectories.
We compared CILO with a baseline and two leading imitation learning methods in
five environments. It had the best overall performance of all methods in all
environments, outperforming the expert in two of them.
