Robotics: The Age of Robot Software is Here

Robotics: The Age of Robot Software is Here

Robotic hardware has arrived, and machines are currently working hard in a myriad of industries including healthcare, manufacturing and many more. However, the robots of today are yet to be the stuff we see on science fiction films. While they are still not good at adaptation and they struggle with tasks that require human interactions, robots are only capable of performing dangerous or tiresome repetitive work.

For robots to reach their full potential, it is time for the software that controls them to catch up with the capabilities of current hardware. As we speak, researchers are working on this challenge, leveraging everything from Big Data to machine learning to artificial intelligence to train robotic machines better and integrate them more seamlessly into daily life.

Robots need to be partners and not tools

For robotic machines to become an autonomous part of the workforce, they need advances robot software to help them grow better at interacting and working alongside their human coworkers via a process that experts call cobotics. This is literally a human-robot collaboration.

The one major challenge faced by cobotics is the fact that humans and robots tend to have overlapping skill sets. For this reason, roboticists need to figure out which tasks they assign to robots and which ones they leave for humans. Cobotics isn’t solely a question of creating robots that handle tasks for us, but rather for making the machines flexible enough to know when to step in and help and when to let us take over.

Teaching the robots

Deep learning is a neural network-based approach to machine learning that makes use of large data sets of today to train robots on behavior. By using this massive sets of data, programmers are now able to improve the robot’s natural language processing, object recognition and image classification among others thus resulting in smarter machines. Ideally, Deep Learning is where machine learning and artificial intelligence comes into play.

The critical part about Deep Learning is training because it is where you’re exposing a neural network to the sort of data that you want it to learn. If you want your robotic machine to learn how to detect widgets in a factory, to detect cars or detect people, all you simply have to do is show many instances of that data throughout the process. This way, the robot learns how to distinguish between different types of widgets in a factory, people or cars.

At the moment, robots know how to pick up a production piece and move it from one place and put it down on another. However, these machines cannot deal well with things such as changes to a production line, dynamic lighting or changing environments. If robots could be smarter about working alongside humans and dealing with more dynamic situations, there are plenty of opportunities to automate a plethora of things throughout the entire industrial supply chain. However, all thanks to cloud computing and big data, the process by which artificial intelligence becomes intelligent are accelerating.

The rise of robots

There is of discussion on robots teaching robots and Deep Learning that is complete without addressing the risk factors that are associated with having responsive, autonomous robots near human workers. By both science and definition, robotic machines are more resilient and stronger than the average human. This creates a potential danger if a potential threat or other breakdown is to occur in the working relationship between humans and robots.

Autonomous driving is an excellent example of this because a person in a self-driving car is essentially sitting inside a robot that is entirely in control of the situation. This makes the possibility of an accident unacceptable thus roboticists need to build in many layers of safeguards.

The process of transitioning to fully interactive robots might be a bit methodical and slow since it takes time and careful effort.

Categories: Technology

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