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Robot in Society: A Question of Interface 17

search space for learning. From a developmental psychology perspective, building systems
that learn from humans allows us to investigate a minimal set of competencies necessary
for social learning.
By positing the presence of a human that is motivated to help the robot learn the task at
hand, a powerful set of constraints can be introduced to the learning problem. A good teacher
is very perceptive to the limitations of the learner and scales the instruction accordingly. As
the learner’s performance improves, the instructor incrementally increases the complexity
of the task. In this way, the learner is competent but slightly challenged—a condition
amenable to successful learning. This type of learning environment captures key aspects
of the learning environment of human infants, who constantly benefit from the help and
encouragement of their caregivers. An analogous approach could facilitate a robot’s ability
to acquire more complex tasks in more complex environments. Keeping this goal in mind,
outlined below are three key challenges of robot learning, and how social interaction can
be used to address them in interesting ways (Breazeal & Scassellati, 2002).

Knowing What Matters
Faced with an incoming stream of sensory data, a robot (the learner) must figure out which
of its myriad of perceptions are relevant to learning the task. As the perceptual abilities of a
robot increase, the search space becomes enormous. If the robot could narrow in on those few
relevant perceptions, the learning problem would become significantly more manageable.
Knowing what matters when learning a task is fundamentally a problem of determining
saliency. Objects can gain saliency (that is, they become the target of attention) through a
variety of means. At times, objects are salient because of their inherent properties; objects
that move quickly, objects that have bright colors, and objects that are shaped like faces are
all likely to attract attention. We call these properties inherent rather than intrinsic because
they are perceptual properties, and thus are observer-dependent rather than a quality of an
external object. Objects become salient through contextual effects. The current motivational
state, emotional state, and knowledge of the learner can impact saliency. For example, when
the learner is hungry, images of food will have higher saliency than otherwise. Objects can
also become salient if they are the focus of the instructor’s attention. For example, if the
human is staring intently at a specific object, that object may become a salient part of the
scene even if it is otherwise uninteresting. People naturally attend to the key aspects of
a task while performing that task. By directing the robot’s own attention to the object of
the instructor’s attention, the robot would automatically attend to the critical aspects of the
task. Hence, a human instructor could indicate what features the robot should attend to as
it learns how to perform the task. Also, in the case of social instruction, the robot’s gaze
direction could serve as an important feedback signal for the instructor.