Robots have outsmarted the various challenges we face in our real world. During this pandemic situation, it has gone through great evolution, aiding humans with its exciting applications. We have witnessed Robots working in enclosed spaces like Airports, Hospitals, or Hotels but, now Robots are getting ready to mingle among the crowds.
The Artificial Intelligence of Robots never fails to fascinate humans. Many countries have deployed the research center for Robotics and focus on empowering AI technologies. The space between humans and Robots are lessening over time. Even then there exists a layer of safety concern while interacting with Robots. Humans are not ready to mingle with Robots because of the anticipation towards the safety measures. It is considered to be the biggest barrier while executing Robots in the real world. But the limitation has been overcome by the Researchers from Stanford along with the Toyota Research Institute. They both have developed a new framework that will take care of these safety measures.
The research team has deployed a framework that combines machine learning algorithms with risk-sensitive control. They have designed the system in a way to avoid accidents while Robots interact with the human in an open and crowded environment. Their ultimate aim revolves around the safe interaction of Robots among Humans. The research team focuses on enabling self-driving cars and other Robots to be function safely in the open environment and being mindful as well.
One of the major challenges is preventing Robots from colliding with Humans. The machine learning model helps in handy to predict the actions of humans when they interact with Robots. It also estimates the risk of collision with each Robot’s action in real-time. Further, Robots colliding with Humans or any other objects can be solved by deploying an optimal maneuver.
Machine Learning solves most of the complications associated with Human-Robot interactions. The new framework outsmarts the existing model because it works on the prediction of multiple outcomes. The predictions are made according to the dynamic environment estimating how both Robots and Humans influence one another.
Robots will go through a training phase to learn how to behave-like-humans in a crowded environment or similar to the area of operation. Further training, the robots will be placed in the real environment. Robots will move freely among the crowds of the human environment.
