  Human-robot teams involve humans and robots collaborating to achieve tasks
under various environmental conditions. Successful teaming will require robots
to adapt autonomously to a human teammate's internal state. An important
element of such adaptation is the ability to estimate the human teammates'
workload in unknown situations. Existing workload models use machine learning
to model the relationships between physiological metrics and workload; however,
these methods are susceptible to individual differences and are heavily
influenced by other factors. These methods cannot generalize to unknown tasks,
as they rely on standard machine learning approaches that assume data consists
of independent and identically distributed (IID) samples. This assumption does
not necessarily hold for estimating workload for new tasks. A survey of non-IID
machine learning techniques is presented, where commonly used techniques are
evaluated using three criteria: portability, model complexity, and
adaptability. These criteria are used to argue which techniques are most
applicable for estimating workload for unknown tasks in dynamic, real-time
environments.
