Scientists developed a robot that has the potential to imagine the future of their actions, according to a new article published by Science Daily, in December 2017.
Scientists from the University of California, Berkeley, have developed a robotic learning technology that enables robots to imagine the future of their actions. This was created with an aim to enable them to figure out how to manipulate objects they have never encountered before.
This technology could help self-driving cars anticipate future events on the road and produce more intelligent robotic assistants in homes. As of now the current prototype focuses on learning simple manual skills entirely from autonomous play.
This technology called visual foresight, enables robots to predict what their cameras will see if they perform a particular sequence of movements. These robotic imaginations are still relatively simple for now.
“In the same way that we can imagine how our actions will move the objects in our environment, this method can enable a robot to visualize how different behaviors will affect the world around it,” said Sergey Levine, developer of the technology. “This can enable intelligent planning of highly flexible skills in complex real-world situations.”
Deep learning technology is based on convolutional recurrent video prediction, or dynamic neural advection (DNA). DNA-based models predict how pixels in an image will move from one frame to the next based on the robot’s actions. Recent improvements to this class of models, as well as greatly improved planning capabilities, have enabled robotic control based on video prediction to perform increasingly complex tasks, such as sliding toys around obstacles and repositioning multiple objects.
“In that past, robots have learned skills with a human supervisor helping and providing feedback. What makes this work exciting is that the robots can learn a range of visual object manipulation skills entirely on their own,” said Chelsea Finn, a doctoral student in Levine’s lab and inventor of the original DNA model.
The new technology will allow a robot to push objects on a table, further using the learned prediction model to choose motions that will move an object to a desired location. Robots use the learned model from raw camera observations to teach themselves how to avoid obstacles and push objects around obstructions.
Machine learning is a rapidly growing advancement in the field of robotics and is gaining popularity due to increasing inclination towards artificial intelligence, as per Machine Learning Market report published by Coherent Market Insights.