Applying Artificial Intelligence And Machine Learning In The Field Of Robotics

Before we get started with this topic, first we should talk about AI and machine learning and what these topics have to do with Robotics.

Concept of AI and ML in Robotics :  

In today’s world artificial intelligence ( AI ) and machine learning ( ML ) are not new concepts. We have been hearing about them for years, how they would affect us in both, positive as well as negative ways.

In simple words, AI is the ability of a computer that lets the system learn new things and tries to make computers smart. AI is also known as a system that copies human cognition.

Machine learning in other words, is a type of AI that allows softwares to become more smart and accurate at predicting outcomes without even being programmed to do so.

Whereas, Robotics is a branch of computer science ( CS ) as well as of engineering. So, where there is computer science, there is AI and Machine Learning and so AI and ML are the integral parts in the field of Robotics.

So, these were some small descriptions about all the three major topics that we are going to cover up : Artificial Intelligence , Machine Learning and Robotics.

Types and applications of AI in Robotics :

• Weak AI : It is a type of AI which is used to create human simulations of their thoughts and for interaction. In this, the robots do not understand the commands given to them. They just read the commands and retrieve the suitable response respectively. The most common examples of weak AI are Siri and Alexa.
• Strong AI : It is a type of AI which is used by robots who have to perform their own tasks on their own. Once they are programmed to do a particular task, then they do not need any kind of supervision, they can perform the tasks all on their own. It is widely used nowadays as many things are becoming automated day by day. One example that interests people the most is self-driving cars. It is a type of AI which is also used in Humanoids. Strong AI is also used in Robotic Surgeons, and this particular concept of robotic surgeons is getting popular day by day.
• Specialized AI : It is a type of AI which is used by robots who have to perform only specified tasks. These robots can only do some limited specified tasks such as painting, tightening, etc.

So, these were the applications and the types of AI that are used in the field of Robotics.

Applications of Machine Learning in Robotics :

Let us now discuss some of the applications of machine learning in robotics.

1. Computer Vision : The main intention of integration of Computer Vision (CV) in Artificial Intelligence is to make and perform visual perception that can visualize and analyse the situation and perform the task on its own without the help of humans. This complete procedure involves certain methods like, obtaining datasets, processing and analyzing them, and also understanding the digital images to make use of them effectively in the real-world. It is really helpful in the field of Robotics, especially in the case of self-driving cars and in those devices in which vision is required.
2. Imitation Learning : It is related to observational learning i.e. a behavior that is exhibited by infants. Nowadays, it has become an integral part of robotics which characterizes mobility outside the factory setting. For example, programming by demonstration that is usually applied by CMU and other related organizations that work in the field of humanoid robotics.
3. Self-Supervised learning : It is a type of learning approach that enables  the robots to generate their own examples for training to improve themselves. It includes data and apriori One example of self-supervised learning is Watch-Bot, another example of this system is road detection algorithm and autonomous learning based programs.
4. Assistive and Medical Technologies : It is a device that is programmed to sense, process the information gathered, and perform actions that help people that are suffering from Robots trained for movement theory provide diagnostic benefits.
5. Multi Agent Learning : Multi agent learning has many components, out of which the two key components are coordination and negotiation. It involves robots based on machine learning that are trained and programmed to adapt to landscape shifting to find equilibrium strategies. One example of multi-agent learning are no regret learning tools, that involve algorithms to boost learning outcomes, and another example is market based, distributed control system.

So, these were some of the uses of machine learning in the field of Robotics.

This also helps in robotic process automation, if you don’t know what robotic process automation is then, don’t  worry, I have got you covered.

Robotic Process Automation (RPA) is a software based technology that helps software robots  to build, deploy, and manage the emulation of human actions by interacting with digital systems and software. Software based robots can understand what’s written on the screen, navigate systems, identify the data  and extract the same, and also perform a wide range of actions that are already defined. So, basically software based robots can do these activities that humans do but the only difference is that it is faster and maybe more efficient than people, and the best part is that software robots do all these tasks without the need to get up and stretch for a coffee break.

RPA is not similar to AI, so do not get misled.

4 Precepts of Man-made brainpower and AI in Advanced mechanics
There are four areas of mechanical cycles that artificial intelligence and AI are influencing to make current applications more productive and beneficial. The extent of artificial intelligence in advanced mechanics incorporates:

Vision – computer based intelligence is assisting robots with identifying things they’ve never seen and perceive objects with far more significant subtlety.
Getting a handle on – robots are likewise getting a handle on things they’ve never seen before with computer based intelligence and AI assisting them with deciding the best position and direction to get a handle on an item.
Movement Control – AI assists robots with dynamic communication and snag evasion to keep up with efficiency.
Information – simulated intelligence and AI both assist robots with understanding physical and calculated information examples to be proactive and act as needs be.

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