Making inanimate objects move and make decisions on their own has been one of humanity’s dreams since antiquity. Stories of a flying wooden dove constructed by Arychtas of Tarentum in the 4th century BCE were one of the earliest examples of how humanity has been dreaming of constructing an autonomous, non-human being either then or in the future.
The dream is so wide and persistent, that it provided fuel for some of the most audacious hoaxes. The Mechanical Turk was an automaton designed to impress Empress Maria Theresa of Austria – a mechanical, non-human chess player, moved by a system of levers and cogs, able to compete with celebrities of the era – including Napoleon I of France and Benjamin Franklin.
In reality, there was a master chess player hidden within the machine. But competing against one’s fellow man was not even half as interesting as competing with a machine. Today computers play chess (and Go and Starcraft) at a godlike level, introducing a new era of robots performing their tasks alongside humans and drawing this ambition even closer.
Why AI matters in robotics
AI is increasingly important for modern robotics, as it is to other industries and markets. According to Allied Market Research, Artificial Intelligence in the Robotics, Aerospace and Defense market is estimated to reach $35.9 billion by the end of 2031. The same industry generated $17.2 billion in 2021.
Interestingly, the connections between robotics and AI are more complex and subtle. And not only the robots get smarter with AI support. The companies that support muscles with pistons on their assembly lines are more eager to adopt artificial neural networks to work alongside natural ones.
Companies that are already using robotics in industrial automation are proving eager and plan to use AI in their decision-making processes.
Advanced AI technologies themselves can be applied in robotics in various ways. But let’s start from the basics.
Core technologies for advanced AI robots
Artificial Intelligence is an umbrella term for a wide range of technologies that support business in many ways, comparable to other umbrella terms like the “steam powered engine” or “web technologies.” In the case of robotics, these more narrow categories of what exactly can be used include:
Computer Vision
Obviously, creators expect robots to interact with the world around them. For commonly used industrial robots, this requires the environment to be predictable and static so the machine may perform a repetitive task.
Equipping robots with modern computer vision software, video cameras and sensors enables them to perform tasks in less controlled environments. Autonomous cars and vehicles are a great example of this. Using sophisticated means of gathering data about their surroundings, AI robots (robo-cars?), gain the ability to operate in an environment full of other autonomous and man-controlled cars, pedestrians, bikers, and tons of other completely unpredictable things.
Tooploox proudly contributed to the development of robotic computer vision, supporting Light’s AI-enabled computer vision system for autonomous vehicles, working on both software and hardware components.
Natural Language Processing (NLP)
While computer vision supports robots in interacting with their environment, Natural Language Processing helps them to interact with humans. Amazon’s Alexa, Siri or Google Assistant are perfect examples of using NLP in human-robot interactions. The Lynx robot is a good example of how this may work in the real world, with a little humanoid robot walking around and having interactions with the user via Alexa.
Reinforcement Learning
Reinforcement Learning is a deep learning paradigm where the neural network learns not from pre-prepared data (a dataset) but from interactions with the environment, usually in a simulated world. A good example comes from Tooploox’s work where a crude oil refinery manager was trained using reinforcement learning in a simulator that generated the data the system operates on.
Reinforcement Learning enables machines to work on more complex tasks that require them to analyze multiple streams of data and make decisions based on that data. In robotics, autonomous vehicles are a perfect example, but as the case study linked above shows, it may be used in multiple other industries.
Applications of AI in Robotics
As it is with AI, Robotics is just an umbrella term for a set of technologies and tools that may be applied in various industries and fields. Although the combination of AI and robotics may be applied in ways unimaginable, the most interesting application examples include:
Manufacturing
Manufacturing is the most obvious use case for AI powered robots, with mechanical arms and systems supporting processes. Using AI may speed up innovation in more exotic or niche product manufacturing. Also, using Artificial Intelligence may impact how already existing workflows are executed.
A good example of the transformative impact of AI and robotics combined comes from Tooploox’s Workshop 4.0, where robotic arms were empowered with computer vision tools that gave them the ability to grab wooden panels of various shapes, sizes, and positioning.
Healthcare and rehabilitation
AI has been extensively tested in supporting the diagnosis process. But AI is also entering other fields of medicine, even those with robots directly involved. The DaVinci surgical robot system is currently using AI-powered tools to enhance surgeon’s performance. Having hardware video sensors that are far more accurate than the human senses, the AI-powered robots are a new power in the hands of surgeons and medical workers that help to save human lives.
Logistics and supply chain
Companies are experimenting with autonomous drones and vehicles in delivery, looking for ways to save on last-mile deliveries. Delivery robots designed by Serve Robotics are a perfect example of combining robotics with AI in logistics and supply chain management to solve problems faced by the industry.
The introduction of fully automated and autonomous cars will push the revolution in logistics even further. A driverless truck needs no rest, no break, no vacation, cannot be sick, and is not missing its family. Also, humanoid robots may be a great aid in labor-heavy environments, like warehouses.
Agriculture
The same approach can be seen in agriculture, where machines and AI robots support human labor. According to University of Illinois data,the average age of US farmers is 58.1, and that value has risen since the 2017 census, where the average farmer was 0.6 years younger. In 1945, an average farmer was 9.4 years younger as compared to 2024.
Using new, sophisticated machines and autonomous farming equipment is a step toward reducing the impact of shifting workforce from agriculture to other sectors and provides the ability to mitigate demographic risks in this industry.
Challenges for AI in Robotics
As mentioned in the preface, the dream of creating an artificial, intelligent entity comes with multiple risks and challenges to consider. The top of them include:
Safety and reliability
Apart from the apocalyptic scenarios of AI robots going rogue, there are multiple more mundane risks associated with creating increasingly sophisticated machines. These may come from insufficient safety measures and the accidents that may occur. Ensuring the safety of all people interacting with the robot is one of key challenges to solve.
Ethical and social implications
Artificial Intelligence used in robotics faces comparable challenges to other instances. Robots may suffer from biased datasets and deliver unfair decisions.
Also, the growing number of intelligent devices in houses and companies puts more pressure on cybersecurity teams and specialists. From privacy breaches to ransomware attacks, the increased number of robots may pose additional risks.
Last but not least, with robots entering spaces like healthcare or home appliances, their responsibility for human well-being increases.
Summary
Implementing AI is a logical next step on the way to developing machines that support everyday tasks, today and in the near future alike, bringing new opportunities for society.
Automating more tasks lets companies reduce costs, save money, and shift the workforce to solve more cognitive-demanding tasks, leaving boredom, danger and tediousness to a non-human workforce.
Tackling the narrowness and developing more general purpose machines further supports this goal. With the intersection of good software, hardware and engineering, no one is able to predict the future – and the limits are only in our imagination.