Body language says a lot about a person. If your guest says he is comfortable, but you see him sitting with his arms folded and his jaw closed, you are questioning his honesty. This is because as humans we have made keen observations all our lives to understand body language and what it conveys. But when it comes to robots, they might think the guest is comfortable saying so audibly. With advances in computer vision and facial recognition technology, robots are unable to pick up subtle body movements.
To coexist, robots must understand social cues
Researchers at Carnegie Mellon University have developed a body tracking system to combat this problem. OpenPose is a system that can track body movements such as those of the hands and face in real time. This system uses computer vision and machine learning to process video frames. It can track the movements of multiple people at the same time, which will improve human-robot compatibility and give way to more augmented reality games and intuitive user interfaces.
If you think a robot that tracks the user’s head, torso and limbs is sophisticated, the OpenPose System can also track individual fingers and their movements. To make this possible, researchers used Panoptic Studio, a dome with 500 cameras used to record body postures from different angles. These images were used to build the data set for the system.
All those different images were then passed through a key point detector that helped identify and label the body parts. OpenPose learns to associate the body parts with its individuals, which makes it possible to track multiple people without creating chaos about whose hand is true.
Initially, images in the dome were captured in 2D, but researchers converted them to 3D to help the body-tracking algorithm understand different poses at different angles. This allows the system to recognize what the person’s hand looks like, even if something obstructs the system’s view. Since OpenPose has all this data to rely on, it can run with a single camera and laptop rather than a camera-lined dome, making this technology more accessible.
As with OpenPose, scientists are working to create more such empathetic robotic systems that can read gesture cues. Another example is a robot called Forpheus that does more than just play table tennis. It reads body language to get a glimpse of its opponents’ ability and offers advice and encouragement. It will try to understand your mood and your playing ability and predict a little bit about your next recording, said Keith Kersten of Omron Automation, a Japanese company that developed Forpheus.
According to researchers who created OpenPose, this type of machine learning technology can be used to enable all kinds of interactions between humans and machines. It can enhance VR experiences by detecting finger movements of the users without additional hardware attached to the users such as gloves or adhesive sensors.
There is a possibility of a future where humans will have robots as companions at home and at work. With these advancements, humans can have more natural interactions with robots. You can tell a home robot to pick up something by pointing at it and the machine will understand what you are pointing at.
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