  This paper considers the motion control and task planning problem of mobile
robots under complex high-level tasks and human initiatives. The assigned task
is specified as Linear Temporal Logic (LTL) formulas that consist of hard and
soft constraints. The human initiative influences the robot autonomy in two
explicit ways: with additive terms in the continuous controller and with
contingent task assignments. We propose an online coordination scheme that
encapsulates (i) a mixed-initiative continuous controller that ensures all-time
safety despite of possible human errors, (ii) a plan adaptation scheme that
accommodates new features discovered in the workspace and short-term tasks
assigned by the operator during run time, and (iii) an iterative inverse
reinforcement learning (IRL) algorithm that allows the robot to asymptotically
learn the human preference on the parameters during the plan synthesis. The
results are demonstrated by both realistic human-in-the-loop simulations and
experiments.
