Industries are rapidly advancing. With growing adaptation and accessibility of state-of-the-art technologies, various industries’ production innovation and R&D technology are becoming very advanced, albeit more complex. However, with technologies getting more complex, they are also getting easier to adapt. So laden with numerous possibilities and opportunities, industries are adopting digital technologies in their industrial application to reap these lucrative advantages as deep learning boosts robot picking flexibility.
The ultimate pursuit of automation in industries and production goes through the path of intelligent and smart robots. With more demanding industries, newer and better robots can perform various industrial applications more smoothly and efficiently. But as industries expand their reach into more fields/sectors, they need robots to achieve even more different tasks in different environments.
This broad spectrum of need for the usability of robots leads to robotic technology not being able to keep up with the demand. Hence, traditional methods and approaches to robotics must be let go to introduce new and better techniques to robotic technology. Within the advent of digital technology lies more possibilities for robotics that are even unseen before.
Digital technologies and platforms like Robotic Simulation Services, Offline Programming, Augmented Reality, Virtual Reality, and Artificial Intelligence take the world by storm. They are now in integration or development for almost every industry possible. The robotics industry also is not lagging in this aspect, with robotic manufacturers or various services providers already utilizing these technologies to propel robotics further. Deep learning is one of the technologies in use, with much anticipation and exciting possibilities, within the robotic industry.
Let's talk about Deep Learning
Deep learning is a type of Artificial Intelligence, or more so a kind of Machine Learning approach. In the broader AI paradigm, Machine Learning is a subset of AI that refers to an AI system that can learn with the help of data instead of developers having to code it. ML is an approach to AI that enables various algorithms to remember from data, i.e., training data consisting of input and output data, to infer a pattern or a “knowledge” in the input data about the output. With this knowledge, ML algorithms can effectively predict the outcomes with the analysis of input data.
Deep Learning is a similar approach. It's a family of algorithms in the machine learning paradigm based upon Artificial Neural Networks (ANNs). These ANNs in deep learning can perform representation learning. Representation learning is a method in which systems detect or infer a pattern or representation, i.e., features in the input data for feature detection or classification. Hence, computer science also defines it as feature learning since it detects features from raw data and uses them to perform some specific task.
Deep learning boosts robotic picking flexibility with its data by effectively imitating how intelligent creatures like humans gain knowledge and do certain things. In deep understanding, a system takes in input data and tries to infer a pattern or detect some specific feature in that data. This “learning” approach is known as deep learning. Furthermore, education can also be either supervised, unsupervised or semi-supervised.
These are various deep learning architectures that researchers combine up with various other computer techniques and technologies to enable different features and functions in robotics: deep neural networks, recurrent neural networks, convolutional neural networks. Deep reinforcement learning and deep belief networks are various architectures in deep learning—robotic technology pairs up these architectures with different hardware and technologies to build various robotic functions.
For instance, robotic researchers and developers use convolutional neural networks for computer vision with cameras and other sensors to give visual information like depth. Likewise, different architectures enable different computer application fields like speech recognition, natural language processing, image analysis, bioinformatics, etc. Moreover, these applications are often in use for various purposes within other industrial areas.
Why Deep Learning Boosts Robotic Picking Flexibility?
In robotics, one of the most complex things to perfect is its ability to pick things up. For human beings, picking items seems very easy. However, seemingly effortless things with biological creatures are not always similar to robotics and computer systems.
Thus, although it may seem that picking items up is easy, it is not the case. The complex interworking of different systems together to perform even a simple task is very hard for computers. For instance, to first pick things up, you need to know what you are picking.
This part is usually straightforward since, for example, you can tell a computer that the stuff it's gathering is in a specific location. But the hard part comes when it's doing the actual picking. For example, how is it even going to pick the object? Even in a single production environment, there are a variety of things with different shapes and sizes. In addition, objects have different textures, structures, and a specific suitable picking spot.
We can undoubtedly program a robot to utilize information about a particular object and a suitable method to pick the thing, but programming it to select it is challenging. Relatively, programming a robot to choose only a single type of object can be easy, but you would need other robots for different kinds of things/products. So this is certainly not an effective method to accomplish this.
Furthermore, products and objects may behave differently in different environments, creating complexities in ways deep learning boosts picking flexibilities. For instance, a product with a smooth surface can be slippery to grab or hold onto in a humid environment. Moreover, picking other objects in different backgrounds requires the robot developer to program the robot for various environments and various things. Along with this, considering the wide range of products, this problem quickly becomes substantially huge.
One of the enormous complexities we are not even exploring yet remains motor skills. Programming a robot to perform specific motor skills and functions is one of the vastest complexities of the robot development paradigm. Even to grant them specific motor functions is very hard. That's why it's a huge deal, even if a robot can perform simple tasks like holding a cup, walking, etc. However, now you can certainly deal with these problems through various means.
For instance, a robot that needs to move can have wheels. A robot that does not have to move but grab onto things can have arms on a fixed body. But these solutions are also tough to implement. Add this to the use case, such as a moving robot that has to move on an uneven surface or a wrong road or even locations where there are no roads, i.e., hills, rocky places, etc. Then this problem becomes substantially more challenging. Similarly, for industrial robots, picking different products and objects is also a complex problem due to different environments and types of things it has to deal with in a particular manner.
Apart from these problems, one primary concern is how deep learning boosts robotic picking flexibility, computer vision. A robot needs to see the object it's picking up. Recognizing a thing insight is a significant feat of computer vision that is currently possible with a massive range of solutions available. But simply recognizing an object is enough to interact with the thing. The robot has to know what object it's looking at and determine how it will pick it up. It again involves problems regarding the size, shape, texture, and structure of the object or product.
In hindsight of all these problems, an industrial robot capable of gripping and interacting with different types of objects or products with other characteristics and properties in different conditions or environments is tough to build. Consequently, it is one of the biggest problems in the industrial robotic plane. It is where deep learning comes into play.
We can use various deep learning techniques to teach a system to recognize and interact with an object. Using deep learning methods, we can use data from multiple production sites, companies, and industries of interaction and manipulation of various things and products for training the system. This data can effectively help a deep learning model to “learn” how to pick different objects in different environments in various particular ways.
The initial data can come from systems already proficient in picking and dealing with objects, which would help in how deep learning boosts robotic picking flexibility. For instance, there is data with humans picking up things. These specialized robots pick only a specific object or interact with them, or even human operators that operate machines to pick up different objects. After data collection of these types, a robot with a deep learning system can go through a training process to effectively learn how to replicate the task or perform it more efficiently.
With this, data collection is complete from a specific specialized robot and for different machines. Moreover, developers and researchers can share and augment such data for training there be used robots for broader use cases and even interact and manipulate objects they are yet to interact with. The possibilities are endless as deep learning boosts robot picking flexibility. As a result, developers can build with a wide range of picking flexibility that can help an industry drive itself towards the end goal of automation. It is why companies like FS Studio provide various services regarding robots and AI tools like deep learning. With decades of collective experience and knowledge with a wide range of expertise, FS Studio provides deep learning services for various robots and other innovative services like Robot Simulation Services, Offline Programming Solutions, and the integration of innovative technologies like AR and VR in different systems.