As a non-technical user of AI, it’s easy to assume that all AI works the same way – fast learning, absorbing huge volumes of information and rapidly improving.
But the kind of learning that powers large language models like ChatGPT doesn’t translate neatly into the physical world. Robots, for example, don’t learn by reading, they learn by interacting with their environment – just like toddlers.
That means moving through a space, responding to changing conditions and gradually improving through experience.
From code to crawling
CSIRO robotics researcher Brendan Tidd explained that this distinction helps us understand why robotics is progressing more cautiously than its digital AI counterparts – and why researchers often equate teaching a robot to teaching a child, rather than programming a machine.
"Toddlers don't just stand up and walk perfectly one day. First, they learn to crawl, then to pull themselves up and finally they take those first teetering steps. More than likely, those first steps end in tears because they lack the coordination or experience to walk steadily. It's similar with robots - without the tears of course - as trial and error becomes valuable learning experience," said Dr Tidd.
The real world sets the pace
At the heart of the challenge is the environment itself.
“The real world is much more diverse and complex than language, there is much more structure in language. In text-based systems, errors are relatively low-stakes. A model can generate an incorrect response, adjust and try again almost instantly. For robots, every action has a tangible outcome,” said Dr Tidd.
“Unlike text, robots don’t have the opportunity to hallucinate and get part of the answer correct. You can’t immediately recover from an incorrect action the robot takes.”
When something goes wrong, the system has to work through the consequences – whether that’s reattempting a movement, resetting the task or incorporating that outcome into future learning. As a result, progress tends to be incremental and closely managed.
Why “trying again” isn’t simple
A fundamental difference between digital and physical AI is that there’s no easy or immediate reset.
“When it comes to training robots, many tasks are dynamic and interactive. Robots often operate in environments that change, so each attempt alters the starting conditions for the next. Even small variations like lighting, positioning or object type can influence outcomes,” said Dr Tidd.
In practice, learning is supported by carefully structured setups and human oversight. In the same way a carer might teach a young child, researchers design appropriate training environments, guide behaviour when needed and ensure tasks can be repeated in a controlled way.
This reveals a constraint that shapes the entire field of robotics: real-world experience takes real time.
“One minute of data takes one minute of real time. Unlike digital systems that can process vast datasets quickly, robots gather experience step by step, much like a toddler – which places natural limits on how quickly they can learn,” noted Dr Tidd.
A significant part of the learning process involves human demonstration.
“The best kind of feedback is the human taking over when the robot is struggling to complete a task. This is known as ‘human-in-the-loop' and allows robots to safely improve while maintaining human oversight,” he said.
This process also requires time, expertise and careful system design – and reinforces why progress in robotics tends to be steady rather than rapid.
Trial and error, with boundaries
As robots develop, they move beyond imitation into trial-and-error learning, often guided by reinforcement learning techniques. This is similar to a child moving from the sensorimotor phase of cognitive development (exploration, memory and recognition, cause and effect) to the sorting and matching, problem solving phase. This learning pattern often repeats as we grow - imitation first, then trial and error.
“Reinforcement learning is the idea that the robot takes an action with some level of surprise or unexpectedness and is evaluated on whether it did better or worse than expected,” said Tidd.
“However, this style of learning is tightly controlled – and usually, robots are able to do the task only some of the time.”
Maintaining that balance – improving performance while operating safely – is fundamental to real-world robotics. It’s also why progress relies on combining approaches such as simulation, human demonstration and carefully controlled testing.
From research to real-world impact
Despite rapid progress in AI, robotics remains an area where practical capability is still catching up to ambition.
“Robots are still painfully slow and hard to work with for tasks that are trivial for humans – like brushing hair, playing Jenga or replacing a screw,” said Dr Tidd.
Rather than displacing human workers, many current robotics applications are focused on areas where automation can make work safer, more reliable or easier to carry out.
These are often environments that are remote, physically demanding or hazardous – such as agriculture, mining and infrastructure inspection – where consistent access and safety are ongoing challenges.
“An important measure of success in robotics is identifying where systems can operate effectively without placing people in harm’s way. For example, mining can be made safer through autonomy, by taking humans out of risky conditions,” said Dr Tidd.
In these settings, robots are designed to complement human work rather than replace it. They take on repetitive, high-risk or hard-to-access tasks, allowing people to focus on oversight, decision-making and more specialised work that benefits from human judgement.
A different kind of AI story
While digital AI has advanced rapidly, robotics is shaped by a different code of practice – physics, safety and the unpredictability of the real world.
There’s still a clear gap between what robots can demonstrate in controlled settings and what they can reliably do day to day. Closing that gap is less about speed, and more about consistency, reliability and trust.
For researchers like Dr Tidd, the goal isn’t to remove humans from the equation, but to rethink how people and machines work together.
“We want to give people the chance to do more inspiring, insightful and impactful work,” he concluded.
And that will be measured not by how quickly robots arrive, but by how usefully they integrate into the world around us.