IEEE Transactions on Systems, Man,
and Cybernetics – Part C: Applications and Reviews, Vol.37, NO.4, July 2007
Summary
One of the key issues in human-robot interaction is
which tasks is better to be performed by humans, by robots or by a combination
of the two and with which level of cooperation. The paper discusses an approach
for predicting system performance resulting from humans and robots preforming
repetitive tasks in a collaborative
manner. Two are the main factors affecting performance: the
approximation of the decline in performance associated with the constant
mental/resource load required to complete a task and the quantification of how
well an agent achieves a certain task. It is known that automation systems are
highly discouraged in presence of unexpected events or uncertainty, where
cooperation with human can improve the overall performance of the system. The
author introduces HumAns (Human Automantion system performance), that evaluates
the effects of workload on human performance, and estimates performance derived
from task allocation. The procedure to be followed consists of 4 steps: 1) decompose the scenario in primitive major
functional tasks; 2) estimate the performance of both human and robot; 3)
calculate the performance score based on satisfaction and effects on both human
and robot; 4) compute a composite task score to enable tradeoff studies in
order to allocate tasks between humans and robots.
The method used for
scenario decomposition is the task diagram interview sequence (part of the
applied cognitive task, ACTA, technique), so that the scenario is broken into 3
to 6 functional primitives, then the cognitive skill/mental demand are derived
(through a classification which creates 3 macro-levels: perception, cognition
and motor activities) and a task diagram can then give an overview. The
primitives are selected independently from each other to emphasize different
aspects. Performance metrics are measured on workload values and execution time
components, the first is relative to decline in performance associated with
mental/resource load required for task completion. The performance metrics are
performed for each primitive found previously. ACT-R is the framework used for
modeling human cognition in this case (it studies how it works in different
scenarios, based on the assumption that come from psychology experiments). At
this point for each primitive there is a time of execution and workload, so
these further steps can be done: 1) calculate the execution rank for each of
the three zones in which the data (execution time vs. ranking value) is divided
(a logarithmic function, given at page 597); 2) calculate the Workload considering it distributed
logarithmically and dependent on the Execution
Rank (page 597); 3) calculating the Composite Task Score. In order to
obtain the Composite Task Score the Markov Decision Process (for which each
agent, either robot or human, is allowed to chose individual actions based on
maximizing an optimization function for the entire system, this incorporates
already both workload ranking values and execution ranking times, the algorithm
is introduced briefly in the paper (page 598).
Key
Concepts
Human-Robot Cooperation, Performances
of Cooperation
Key Results
Two different setups have
been done testing tele-operation control and fully autonomous control. HumAns
is denoting how as time elapses the task score decreased, while for a fully
automated robot the scores stays fairly constant, in the comparison we could
say that for short elapsing times teleoperation is more convenient than full
automation, which is not true in the long and repetitive case. HumAns appears
to be a very important and useful method to predict workload on human and
machine and the consecutive effects in the overall system, still studies on the
effects on accuracy and repeatability have to be performed.
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