Washington, July 11: Scientists have developed a a hyper-realistic Einstein robot which they claim has learned to smile and even make facial expressions through a process of self-guided learning.
A team at California University used machine learning to “empower” their robot to learn to make realistic facial expressions.
The Einstein robot head has about 30 facial muscles, each moved by a tiny servo motor connected to the muscle by a string. Today, a highly trained person must manually set up these kinds of realistic robots so that the servos pull in the right combinations to make specific face expressions.
In order to begin to automate this process, the team looked to both developmental psychology and machine learning.
Developmental psychologists speculate that infants learn to control their bodies through systematic exploratory movements, including babbling to learn to speak. Initially, these movements appear to be executed in a random manner as infants learn to control their bodies and reach for objects.
“We applied this same idea to the problem of a robot learning to make realistic facial expressions,” said Javier Movellan, a team member.
Although their preliminary results are promising, the scientists noted that some of the learned facial expressions were still awkward. One potential explanation is that their model may be too simple to describe the coupled interactions between facial muscles and skin.
To begin the learning process, the team directed the Einstein robot head to twist and turn its face in directions, a process called “body babbling”.
During this period the robot could see itself on a mirror and analyse its own expression using facial expression detection software created at the university. This provided the data necessary for machine learning algorithms to learn a mapping between facial expressions and the movements of the muscle motors.
Once the robot learned the relationship between facial expressions and the muscle movements required to make them, the robot learned to make facial expressions it had never encountered, the scientists said.
“During the experiment, one of the servos burned out due to misconfiguration. We therefore ran the experiment without that servo. We discovered that the model learned to automatically compensate for the missing servo by activating a combination of nearby servos.
–PTI