The development of robotics is a field that merges computer science with engineering and is highly connected to artificial intelligence (AI). While the two studies are separate fields, they hold a symbiotic relationship and have been highly associated with one another since AI and robotics first began in the 1950s and 1960s. 

There are many ways that AI is integrated into robotics, with some of the most advanced innovations being primary components of modern and future robotics. This is because robots require a vast amount of software technology before they are capable of mimicking the humanoid robots that we normally think about in popular sci-fi films and novels. 

Deep Learning and Neural Networks

Deep learning and neural networks are two cornerstones of AI and Machine Learning that mimic the human brain by placing multiple layers filled with processing nodes called neurons inbetween the input and output. This allows algorithms to intake large amounts of data and produce complex outputs. 

In the context of robotics, this gives machines the basic cognitive requirements to execute commands necessary for movement and data processing. This is how robots power their primary decision-making processes, learning from experience and maintaining context, leading to applications involving robotic vision and audio recognition for more advanced input data collection. 

Natural Language Processing

Natural language processing (NLP) is a subset of AI that allows algorithms to interpret and understand human languages. With the proper training, they can read and listen to humans and respond appropriately, capable of maintaining full conversations with participants. 

NLP creates many benefits for human-robot interaction, allowing robots to remove themselves from a set list of commands and respond to natural human instructions. This makes commercial-centric robotics more viable to consumers who prefer to communicate with robots using natural language instead of programming languages. 

These features are especially helpful for the future of customer service and robotic assistants that will eventually be found in hotels and corporate offices. Voice assistants are already popular in devices like Alexa, so it is only natural that they will evolve into larger, more mobile machines. 

Reinforcement Learning

Reinforcement learning is a style of machine learning that places an artificial agent in an environment where they must learn and adapt to the world around them based on certain rules that either reward or penalize the agent. This form of continuous learning has been a considerable innovation in machine learning because of allows machines to learn on their own.

Robotics has the ability to take reinforcement learning a step further by transcending an agent from a virtual environment to our real environment. Instead of learning in a digital landscape, we can make the robot recognize itself as the agent and influence it to learn about our world. 

These advanced robotic systems can lead to massive improvements in AI infrastructure around the world, helping to control autonomous fleets of vehicles or swarms of industrial machines inside warehouses and factories. Over time, their existence in a real-world environment will lead machines to self-learn and adapt to more than just basic commands, but also human interaction.  

Robot Operating System (ROS)

Robot Operating Systems (ROS) are a framework used for writing software for robots. While it is not exactly the same as traditional OS systems, it has become indispensable in the development of robotic innovation, creating a centralized system for data processing and command execution. 

The basic purpose of ROS is to provide a framework for robots to use including hardware abstraction, low-level device control, and message passing between processes. This allows robots to open communication channels between hardware and software similar to drivers. Likewise, more advanced architectures are also modular, allowing developers to create additional code for additional components. 

A robust ROS is important for bridging the gap not only between hardware and software, but AI programs as well. Robots can access AI features like computer vision and image recognition by using ROS publisher-subscriber models that open up communication channels with deep learning models for seamless data transfer. 

Generative Adversarial Networks (GANs)

Generative adversarial networks are a fairly recent innovation in AI that places two AI models against each other. One model, the generator is tasked with creating outputs while the second model, the discriminator, is meant to determine whether the given output is artificially generated or not. The two models then compete, trying to outperform the other by self-improving themselves.

In the context of robotics, GANs are perfect for training robots on large amounts of data because they can generate high-quality outputs at a staggering pace. They are also beneficial because robot training needs to occur in a safe environment where the risk to humans is low. By making simulated environments for reinforcement learning, robots can train themselves and prepare for deployment in the real world with relative ease. 

Evolutionary Algorithms

Evolutionary algorithms are a form of problem-solving techniques for AI that mimic natural evolution. They emulate evolution with self-learning, finding optimized methods of solving complex problems. They do so by utilizing key components of evolution such as selection, crossover, and mutation to find new ways of solving problems and adhering to methods that are more successful than previous attempts. 

Evolutionary algorithms hold tremendous potential in robotics, helping them adapt to the world around them in a similar fashion to all living organisms. While physical evolution is still out of the question because robots are constructed instead of grown, their minds are still able to adapt of evolve to new stimuli and conditions that surround their existence. 

Swarm Intelligence

Swarm intelligence is the collective behavior of a large body of robots and AI systems, mimicking certain behaviors in the natural world such as hive colonies and bird flocks. This allows robotic fleets to become decentralized, all following the same commands as their counterparts without following a singular leader. 

This form of AI is especially useful for robots in warehouses and factories known as cobots that are responsible for shipping and logistics. They can aid humans by moving goods around large facilities, and seamlessly communicating with other cobots.

Swarm intelligence also makes robotic maintenance easier because a single failure in one cobot won’t harm the rest of the fleet. Instead, predictive analytics an help monitor which bots are about to fail and can suggest replacements or repairs in a timely manner. 

Conclusion

Robotics and AI are two aspects of computer science that share a powerful bond. They both pursue the goal of recreating humanity in technological form, with robots being the body and AI being the mind and becoming more present in our modern world. 

As innovations continue, we can expect to see more AI programs integrated into the software of robotics, making them more humanlike than ever before, eventually becoming a part of our human society as commercial products or even a sentient race of their own.