Causal Transfer for Imitation Learning and Decision Making under
  Sensor-shift
  Learning from demonstrations (LfD) is an efficient paradigm to train AI
agents. But major issues arise when there are differences between (a) the
demonstrator's own sensory input, (b) our sensors that observe the demonstrator
and (c) the sensory input of the agent we train. In this paper, we propose a
causal model-based framework for transfer learning under such "sensor-shifts",
for two common LfD tasks: (1) inferring the effect of the demonstrator's
actions and (2) imitation learning. First we rigorously analyze, on the
population-level, to what extent the relevant underlying mechanisms (the action
effects and the demonstrator policy) can be identified and transferred from the
available observations together with prior knowledge of sensor characteristics.
And we device an algorithm to infer these mechanisms. Then we introduce several
proxy methods which are easier to calculate, estimate from finite data and
interpret than the exact solutions, alongside theoretical bounds on their
closeness to the exact ones. We validate our two main methods on simulated and
semi-real world data.
