Our work focuses on  inverse reinforcement learning and imitation learning methods regarding multidimensional trajectories: Given demonstrations of un-labeled and unsegmented trajectories, which may also reveal behavioural preferences, constraints and sub-goals that have to be achieved on-the-go, the goal is to learn safely, effectively (even with few examples), aligned with human preferences and constraints, to behave according to demonstrated behaviour.