  A limitation for collaborative robots (cobots) is their lack of ability to
adapt to human partners, who typically exhibit an immense diversity of
behaviors. We present an autonomous framework as a cobot's real-time
decision-making mechanism to anticipate a variety of human characteristics and
behaviors, including human errors, toward a personalized collaboration. Our
framework handles such behaviors in two levels: 1) short-term human behaviors
are adapted through our novel Anticipatory Partially Observable Markov Decision
Process (A-POMDP) models, covering a human's changing intent (motivation),
availability, and capability; 2) long-term changing human characteristics are
adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that
selects a short-term decision model, e.g., an A-POMDP, according to an estimate
of a human's workplace characteristics, such as her expertise and collaboration
preferences. To design and evaluate our framework over a diversity of human
behaviors, we propose a pipeline where we first train and rigorously test the
framework in simulation over novel human models. Then, we deploy and evaluate
it on our novel physical experiment setup that induces cognitive load on humans
to observe their dynamic behaviors, including their mistakes, and their
changing characteristics such as their expertise. We conduct user studies and
show that our framework effectively collaborates non-stop for hours and adapts
to various changing human behaviors and characteristics in real-time. That
increases the efficiency and naturalness of the collaboration with a higher
perceived collaboration, positive teammate traits, and human trust. We believe
that such an extended human adaptation is key to the long-term use of cobots.
