  Adaptive task planning is fundamental to ensuring effective and seamless
human-robot collaboration. This paper introduces a robot task planning
framework that takes into account both human leading/following preferences and
performance, specifically focusing on task allocation and scheduling in
collaborative settings. We present a proactive task allocation approach with
three primary objectives: enhancing team performance, incorporating human
preferences, and upholding a positive human perception of the robot and the
collaborative experience. Through a user study, involving an autonomous mobile
manipulator robot working alongside participants in a collaborative scenario,
we confirm that the task planning framework successfully attains all three
intended goals, thereby contributing to the advancement of adaptive task
planning in human-robot collaboration. This paper mainly focuses on the first
two objectives, and we discuss the third objective, participants' perception of
the robot, tasks, and collaboration in a companion paper.
