Page 37 - Designing Sociable Robots

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18 Chapter 2

Knowing What Action to Try

Once the robot has identified salient aspects of the scene, how does it determine what
actions it should take? As robots become more complex, their repertoire of possible actions
increases. This also contributes to a large search space. If the robot had a way of focusing
on those potentially successful actions, the learning problem would be simplified.
In this case, a human instructor, sharing a similar morphology with the robot, could
provide considerable assistance by demonstrating the appropriate actions to try. The body
mapping problem is challenging, but could provide the robot with a good first attempt. The
similarity in morphology between human and humanoid robot could also make it easier and
more intuitive for the instructor to correct the robot’s errors.

Instructional Feedback

Once a robot can observe an action and attempt to perform it, how can the robot determine
whether or not it has been successful? Further, if the robot has been unsuccessful, how does
it determine which parts of its performance were inadequate? The robot must be able to
identify the desired outcome and to judge how its performance compares to that outcome.
In many situations, this evaluation depends on understanding the goals and intentions of
the instructor as well as the robot’s own internal motivations. Additionally, the robot must
be able to diagnose its errors in order to incrementally improve performance.
The human instructor, however, has a good understanding of the task and knows how to
evaluate the robot’s success and progress. If the instructor could communicate this infor-
mation to the robot in a way that the robot could use, the robot could bootstrap from the
instructor’s evaluation in order to shape its behavior. One way a human instructor could fa-
cilitate the robot’s evaluation process is by providing expressive feedback. The robot could
use this feedback to recognize success and to correct failures. In the case of social instruc-
tion, the difficulty of obtaining success criteria can be simplified by exploiting the natural
structure of social interactions. As the learner acts, the facial expressions (smiles or frowns),
vocalizations, gestures (nodding or shaking of the head), and other actions of the instructor
all provide feedback that allows the learner to determine whether it has achieved the goal.
In addition, as the instructor takes a turn, the instructor often looks to the learner’s face to
determine whether the learner appears confused or understands what is being demonstrated.
The expressive displays of a robot could be used by the instructor to control the rate of
information exchange—to either speed it up, to slow it down, or to elaborate as appropriate.
If the learner appears confused, the instructor can slow down the training scenario until the
learner is ready to proceed. Facial expressions could be an important cue for the instructor as
well as the robot. By regulating the interaction, the instructor could establish an appropriate
learning environment and provide better quality instruction.