  Inverse reinforcement learning is a paradigm motivated by the goal of
learning general reward functions from demonstrated behaviours. Yet the notion
of generality for learnt costs is often evaluated in terms of robustness to
various spatial perturbations only, assuming deployment at fixed speeds of
execution. However, this is impractical in the context of robotics and
building, time-invariant solutions is of crucial importance. In this work, we
propose a formulation that allows us to 1) vary the length of execution by
learning time-invariant costs, and 2) relax the temporal alignment requirements
for learning from demonstration. We apply our method to two different types of
cost formulations and evaluate their performance in the context of learning
reward functions for simulated placement and peg in hole tasks executed on a
7DoF Kuka IIWA arm. Our results show that our approach enables learning
temporally invariant rewards from misaligned demonstration that can also
generalise spatially to out of distribution tasks.
