A robot requires understanding about the environment and his body. It needs to try several different motions and utilize the algorithm. It helps the robot to predict much larger movement with more speed.
Earlier, machine learning was able to understand the data, doing predictions regarding the situation which is among other defined situations. It was not able to use the data and decide any new things accordingly, and make predictions regarding situation outside the defined situation, as they were able to learn the existing data and fits in the environment, without considering how it will work outside the defined situation.
Along with this, gathering enough information for understanding the situation effectively which is efficient in terms of resource and time, and looking for data from risky and extreme situations.
In recent times, former IST Austria postdoc and ISTFELLOW, Georg Martius, and a group leader, Intelligent Systems in Tübingen, MPI, A Phd. student, Subham S. Sahoo, Intelligent Systems, MPI, and professor, Christoph Lampert, IST Austria, made an advanced machine learning methodology which can evaluate the problems, and this is the first machine learning process which can help in extrapolate the undefined situations precisely.
The primary and important feature of the newly introduced method is that it has the steps which help to uncover the real dynamics of the all sorts of situations, it gets the information from the provided data and provides the equation which helps in understand the required physics. Georg Martius says, “When you get to know the equations, then you can say what is going to occur in the entire situation, even if haven’t experience them personally.”
If we put this in simple words, this is the methodology which helps to extrapolate reliably, and make the newly introduced method a best and novel in among all the machine learning methods.