Ai In Robotics: an Assimilation For The Next Phase In Technology

Ai In Robotics: an Assimilation For The Next Phase In Technology

Robotics has been one of the most discussed topics in AI Tech communities. As a term, it indicates automation and the creation of AI-based

The basis of robotics can be traced back to the concept of cybernetics, discussed back in the 1940s. This concept indicated the Control Theory involving the control of the dynamic systems. In the modern era, the theory of control and system has evolved while cybernetics has transformed into other subforms and a prominent merger of computing, artificial intelligence, cognitive science, complexity science, and robotics can be seen today.

Beyond this, in the modern world, the use of artificial intelligence in robotics has powered several business verticals and helped them evolve their processes and become more sophisticated and state-of-the-art.

Robotics with AI and DNNs (Deep Neural Network)

AI is introducing considerable changes across business verticals. Robotics and artificial intelligence have been widely adopted via robotic automation in key areas such as Healthcare, Automotive, Warehousing, Agriculture, and Food, accelerating automation.

On the other hand, tangibly, robotics has emerged as the next phase after AI to leap ahead. For tasks like assembly line checks in an industrial automotive unit or performing a minimally invasive procedure in a medical institution, the presence of robotic automation is worth noting. Robotics has spread its reach wide, specifically across industrial units. In the industrial sector, robotics works mainly with the help of following components:

• Control system for the main functioning

• Sensors for understanding its environment

• Actuators for movement

Moving towards more sophisticated forms of robots which can maneuver to perform human-like tasks and are ideally referred to as humanoids; we can see a merged realm. AI-backed by machine learning approaches has merged with the tangible machine form. There is a clear synergy of multiple elements to perform an action in robotics. A robot is equipped with neural networks, machine perception, NLP, adaptive motor control, and cognitive architecture for movement. There is a control system of the robots that contains the main programming based on which it can perform various tasks.

Robotics makes use of both AI and deep neural network approaches to execute tasks. With relation to the co-related machine learning model, robotics is utilizing the methodologies of self-supervised learning, imitation learning, multi-agent learning, and a significant portion of Principal Component Analysis with computer vision. For instance, the K-means algorithm is used for obstacle detection in robotics and how to overcome the types of obstacles in the pathway; specifically, such robots are designed for delivering customer packages in a locality. These robots are getting trained on data pertaining to the location using ML methodologies and are familiarized to understand the environment, the calibrated physical movement, and the task they are required to accomplish; which can be as simple as delivery of a retail item at the doorstep of the customer.

Furthermore, deep learning has been aligned with robotics to enhance their abilities to process multitudes of data and perform tactical movement and actions. While machine learning enables one core function and learning of the robot or humanoid, deep learning is enabled with multi-layer neural networks for increased performance and faster execution, and well-calibrated moves. To elaborate, in robotics, a deep learning model called autoencoders enables the humanoids to perceive and understand the environment through encoding the environmental observation for internal representation. The encoder is capable of processing heavy-duty high-dimensional data and produces low-dimensional representation to easily interpret the original observations. In a similar way, generative models of deep learning are used in robotics to use observation for making predictions or work without any internal representations, at times.

In the past few years, both AI and DNNs have augmented the learning approaches in robotics, as well as in robotic automation, whether through the use of algorithms or training via deep neural networks.

Progress made so far

Robotics can be regarded as a field with a fair amount of autonomy in human tasks that require the intervention of machines. The sector is expanding and humanoids or robots are in demand as they are capable of performing a variety of tasks like picking up weights, place products, and also do critical tasks like surgeries in the medical sector. They are being developed to do simple human physical actions like sit, stand, lift, grasp, jump and even talk back like us.

Simultaneously, as the fear of robots eroding employment opportunities for humans looms large; the concept of Cobots is also underway, wherein, robots and humans will collaborate to carry out tasks. To contain this fear, an effort is being made to ascertain that robotics aims at reducing dependency on humans for heavy-duty tasks. Also, ways are being explored for providing robots with the ability to process massive amounts of data through Edge computing, targeting robotic capability to make decisions; a considerable amount of attention is been given to end-to-end cybersecurity for ensuring a protected environment for data access to the robots, free from external attacks.

Also Read : How AI Robots Used in Medical Field: Types of Robotics in Healthcare

Concluding note

The field of robotics and Artificial Intelligence empowered with IoT beckons a world where human capabilities and intelligent machines can merge to enable multiple layers of networks, architectures, and systems into one ecosystem.

The tech community has already addressed some huge challenges in robotics such as advanced object recognition, learning in a dynamic environment, human action interpretation, high-level task planning for developing humanoids, walking further on the trajectory of technological advancements, in terms of moving into the human-machine interaction phase; succinct examples of which can be seen through Hanson’s Sophia, MIT’s Robo Dogs, and Honda’s Asimo.

From pre-programmed robots to semi-autonomous bots and autonomous robots and humanoid robots, as of now, the discerning question remains - Will the world witness an evolved breed of robots in action during the human-machine interaction phase?