Scientists can now read robots' minds!
MIT scientists have developed a new system that can visually represent a robot's decision-making process.
Washington: MIT scientists have developed a new system that can visually represent a robot's decision-making process.
The visualisation system combines ceiling-mounted projectors with motion-capture technology and animation software to project a robot's intentions in real time.
The system has been dubbed the "measurable virtual reality (MVR) - a spin on conventional virtual reality that's designed to visualise a robot's "perceptions and understanding of the world," according to Ali-akbar Agha-mohammadi, a postdoc in Massachusetts Institute of Technology's Aerospace Controls Lab, who developed the system with his team.
"Normally, a robot may make some decision, but you can't quite tell what's going on in its mind - why it's choosing a particular path," Agha-mohammadi said.
"But if you can see the robot's plan projected on the ground, you can connect what it perceives with what it does to make sense of its actions," Agha-mohammadi said.
Agha-mohammadi said the system may help speed up the development of self-driving cars, package-delivering drones, and other autonomous, route-planning vehicles.
"As designers, when we can compare the robot's perceptions with how it acts, we can find bugs in our code much faster," he said.
"For example, if we fly a quadrotor, and see something go wrong in its mind, we can terminate the code before it hits the wall, or breaks," he said.
The team developed the system as a way to visually represent the robots' decision-making process. The engineers mounted 18 motion-capture cameras on the ceiling to track multiple robotic vehicles simultaneously.
They then developed computer software that visually renders 'hidden' information, such as a robot's possible routes, and its perception of an obstacle's position.
They projected this information on the ground in real time, as physical robots operated.
The researchers found that by projecting the robots' intentions, they were able to spot problems in the underlying algorithms, and make improvements much faster than before.