Humanoid hard sell: building robots for the manufacturing age

As humanoids move out of the lab and into real-world deployment, some caution that excitement about their capabilities may be outpacing reality.

Stephen Bennington, CEO of British robotics company Q5D, says that with industry deployments, manufacturers shouldn't assume copying the human worker is the best way to automate a job. His advice is to start with the process rather than the robot: when a task isn't tied to human-built environments, a purpose-built machine can be faster, more accurate and significantly cheaper.

In this Q&A, Bennington discusses where humanoid robotics stands, how AI is changing industrial automation and what Western manufacturers can learn from China.

There is a huge amount of excitement about humanoid robots at the moment. Where does the technology genuinely stand, and where is the publicity getting ahead of reality?

Stephen Bennington: The progress is exciting. Humanoid robots are becoming more capable, and we are starting to see trials in industrial environments rather than just impressive demos. But for many applications, I do think the hype is still ahead of reality.

There is, of course, a logical reason for the interest. Our factories, warehouses and tools were designed around people. So, if you can build a robot that can move through those environments and interact with the same equipment, you potentially avoid having to redesign the workplace around the machine.

However, where I would be slightly more cautious is the assumption that copying the human operator is always the best way to automate a job.

Take something as mundane as washing dishes. You could build a very sophisticated humanoid robot that stands at a sink and copies every movement a person makes. Or you can build a dishwasher that isn't constrained by the human form and is therefore focused on ensuring the task is completed as efficiently as possible with minimal disruption to the household.

The same principle applies in manufacturing. If a humanoid is required to move through an environment built for humans using existing tools, then being in a human form makes sense. But if you're designing a manufacturing process from scratch or aren't constrained by those limitations, the solution can be a purpose-built machine that is faster, more accurate and significantly cheaper.

Rather than starting with the robot, manufacturers should start with the process -- looking closely at what they are trying to automate and the most effective way of doing it.

What are the main bottlenecks to deploying these machines in real-world applications?

SB: Some of the hardest tasks to automate are actually the ones that look very ordinary when a person does them.

Wiring is a good example. A human operator can pick up a flexible wire, manipulate it, route it through a complicated shape and make connections almost instinctively. For a robot, flexible materials are difficult because they move and behave differently every time you handle them.

Rather than simply trying to make a robot better at copying those human movements, you can rethink the process itself. Can you break it into different stages? Can you design each stage around what machines are actually good at? That is where I think some of the really interesting advances in industrial robotics will come from.

Another major bottleneck is programming. You can have an extremely capable robot, but someone still has to tell it what to do. AI is changing how that happens by speeding up traditional programming, but you still need training data to train the machine in the first place.

Are there any particular approaches to address these challenges?

SB: There are really two ways of tackling the problem. One is to make the robot more adaptable. That is what we're seeing with humanoids, where AI and large amounts of training data are helping robots understand their environment and learn different tasks.

The other is to redesign the task to make it easier to automate. That's the approach we have taken at Q5D, where we redesign processes around what a machine can do well, thus removing much of the need to manually teach the robot what to do every time the design changes.

I think that points to a broader opportunity in robotics. Flexibility does not have to come entirely from the robot itself. You can also create a highly flexible manufacturing system by combining purpose-built machinery with software that makes it much easier to move from one product to another.

How has AI changed industrial robots’ capabilities?

SB: AI is going to make industrial robots more capable and adaptable, and that applies to far more than just humanoids. Advances in AI can improve everything from inspection to predictive maintenance.

For humanoids, AI is particularly important because of the sheer variety of situations they need to deal with. If you want a robot to perform lots of different jobs, it has to interpret what it sees, understand its surroundings and work out how to respond. That requires a lot of intelligence and a lot of training data.

But those same advances can also improve specialist machines. If a manufacturing system already has digital information specifying what needs to be made and how, AI can be applied to a much more clearly defined problem.

There are also challenges that come with giving robots greater autonomy, particularly around safety. In an industrial environment, you need to be confident that a robot will behave predictably not only during normal operation, but also when something unexpected happens. That becomes even more important when robots are working alongside people.

So, AI will make general-purpose robots more versatile, but it will also raise the capabilities of purpose-built automation. The two will develop alongside each other, with the right choice depending on the task. In some situations, versatility will matter most; in others, manufacturers will prioritize speed, precision, safety and cost.

China has become a major force in industrial robotics. What is driving that, and what should manufacturers in the West learn from it?

SB: What China demonstrates particularly well is the relationship between robotics and manufacturing scale.

It has an enormous industrial base, which creates demand for automation but also provides robotics companies with places to deploy, test and improve their technology. That creates a powerful feedback loop, as more manufacturing creates a bigger market for robotics, while greater adoption of robotics helps make that manufacturing more productive.

For Western economies, I think there is a temptation to focus on who has the most sophisticated robot or the most impressive demo. The more important question is how effectively new technology actually makes its way onto the factory floor.

Ultimately, this is a productivity question, and one that will become increasingly important as populations age and the available workforce shrinks. If Western economies want to bring more manufacturing closer to home and strengthen supply chains, while remaining competitive in relatively high-wage countries, we have to become much better at deploying automation.

That means thinking beyond the technology itself. Manufacturers need the confidence and capital to invest, but the technology also needs to offer a clear enough commercial return to justify changing established production processes.

Looking ahead, how do you expect the robotics industry to evolve as user demands and technology advance?

SB: I suspect the future will be much more varied than the current excitement around humanoids sometimes suggests. We are likely to see different types of robotics developing alongside one another, each suited to different manufacturing problems and evolving user demands.

One of the biggest changes will arguably be in how flexible industrial automation becomes.

Traditional automation is brilliant when you want to make the same thing millions of times, but historically it has been harder to justify when you have lots of variants, shorter production runs or products that are constantly changing. That requires a different approach to automation, in which machines adapt quickly rather than being engineered for a specific task.

Over the next decade, I think the conversation will become less focused on what the robot looks like and more focused on what the manufacturing system can actually deliver. Better software, AI and closer links between digital design and the physical machine will make automation much easier to adapt as products and user requirements change.

Editor’s note: This interview was edited for clarity and conciseness.

Scarlett Evans is a freelance writer with a focus on robotics and emerging technologies. Previously, she was assistant editor at IoT World Today, where she specialized in robotics and smart city technologies. Scarlett also has a background in the mining and resources sector, with experience at Mine Australia, Mine Technology and Power Technology.