I still remember the first time I saw a real humanoid robot. It wasn’t in a movie. It was at a lab at Stanford, and to be honest, it was clumsy. It stumbled, it whirred, and it couldn’t even open a door without fumbling for a good thirty seconds. But I was hooked. The sheer ambition of it—to create a machine in our own image—was intoxicating. That was years ago. Today, as an investor in companies like Anthropic and OpenAI, I see a new wave of robotics that’s less about clumsy lab experiments and more about real-world applications.
People ask me all the time, "Sahin, what's the next big thing?" They expect me to say some new app or a fancy AI model. I tell them, look at the physical world. That's where the next revolution is. And at the heart of it? Humanoid robots.
But building one isn’t like coding an app. It’s a brutal, multidisciplinary challenge. You’re dealing with physics, not just logic. I’ve seen teams burn through millions of dollars and years of their lives to build something that can barely walk a straight line. I’ve also seen scrappy startups create incredible machines on a shoestring budget. The difference isn’t just money. It’s about understanding the core principles. This is the guide I wish I had when I started my journey in robotics.
It All Starts with the Skeleton
Before you even think about the "brain," you need a body. And that starts with the frame. You have a few choices here. You can go with a 3D-printed plastic body, which is cheap and easy to prototype. Or you can go with a more robust metal frame, like aluminum or carbon fiber. I’ve seen both work. The key is to think about the trade-offs.
A plastic body is great for a first prototype. You can iterate quickly, print new parts overnight, and you don’t need a machine shop. But it’s not going to be very durable. If you want your robot to do any real work, you’ll need to upgrade to metal. Aluminum is a good middle ground. It’s relatively lightweight, strong, and not too expensive. Carbon fiber is the gold standard, but it’s also the most expensive. Unless you’re building a high-performance machine for a specific application, I’d stick with aluminum.
I remember one of the early robotics startups I invested in. They spent a year and a half designing the perfect carbon fiber chassis. It was a work of art. But by the time they had it built, their competitors had already launched a product with a simpler, cheaper aluminum frame. They were too focused on perfection and missed the market. Don’t make that mistake. Start simple, and iterate.
The Muscles: Motors and Actuators
This is where things get interesting. The motors and actuators are the "muscles" of your robot. They’re what allow it to move. And they are, without a doubt, one of the hardest parts to get right. You need a combination of power, precision, and speed. And you need to do it all in a compact, lightweight package.
For a long time, the go-to solution was servo motors. They’re cheap, easy to control, and they come in a wide range of sizes and power ratings. But they have their limitations. They’re not very efficient, and they can be noisy. For a small, hobbyist robot, they’re fine. But for a full-sized humanoid, you need something more.
This is where frameless torque motors and coreless motors come in. These are the same types of motors that are used in high-end industrial robots and even in some electric vehicles. They’re incredibly power-dense, efficient, and quiet. But they’re also more expensive and harder to integrate. You can’t just buy one off the shelf and plug it in. You need to design a custom housing and a custom control system.
One of the most impressive teams I’ve seen in this space is a group of engineers who came out of the drone industry. They were used to building incredibly lightweight, powerful motors for their drones. They applied the same principles to building actuators for their humanoid robot, and the results were stunning. Their robot was faster, stronger, and more agile than anything else I had seen at the time.
The Brain: AI and Control Systems
Now for the fun part: the brain. This is where the magic happens. It’s what separates a dumb machine from an intelligent robot. And with the recent advances in AI, the possibilities are endless. You can use a combination of traditional robotics algorithms and modern machine learning techniques to create a robot that can learn, adapt, and interact with the world in a truly intelligent way.
But here’s the thing: you don’t need to build your own AI from scratch. There are plenty of open-source tools and platforms that you can use. Companies like Hugging Face and OpenAI are making it easier than ever to build sophisticated AI systems. I’m an investor in both, and I’ve seen firsthand how they’re democratizing AI.
Your job is to integrate these tools into a cohesive control system. You need a "brain" that can take in sensory information, make decisions, and send commands to the motors. This is a complex software engineering challenge, but it’s also where you can get the most creative. You can experiment with different AI models, different control strategies, and different user interfaces.
One of the most common mistakes I see is teams trying to build a single, monolithic AI that does everything. That’s a recipe for disaster. A much better approach is to use a modular architecture. Have different AI models for different tasks. One for perception, one for navigation, one for manipulation, and so on. This makes your system more robust, more scalable, and easier to debug.
The Senses: Cameras, Lidars, and More
A robot is only as good as its senses. It needs to be able to see, hear, and feel the world around it. This is where sensors come in. You have a wide range of options to choose from, each with its own strengths and weaknesses.
Cameras are the most obvious choice. They’re cheap, they’re high-resolution, and they provide a rich source of information about the world. But they’re also sensitive to lighting conditions, and they can be fooled by shadows and reflections.
Lidar is another popular choice. It uses lasers to create a 3D map of the environment. It’s much more accurate than cameras, and it works in any lighting condition. But it’s also more expensive and lower resolution.
For a truly robust system, you need to use a combination of sensors. This is called sensor fusion. By combining the data from multiple sensors, you can get a much more accurate and complete picture of the world. For example, you can use a camera to identify objects and a lidar to determine their exact location.
I remember a demo I saw a few years ago. It was a humanoid robot that was supposed to be able to navigate a cluttered room. But it kept bumping into things. The problem was that it was only using a single camera. The team added a lidar, and suddenly, the robot was able to navigate the room flawlessly. It was a powerful lesson in the importance of sensor fusion.
The Challenges Ahead
Building a humanoid robot is not for the faint of heart. There are still many challenges to overcome. Battery life is a big one. These robots consume a lot of power, and it’s a constant struggle to keep them running for more than a few hours at a time. Dexterity is another. We still haven’t figured out how to build hands that can match the dexterity of a human hand. And then there’s the cost. These robots are still incredibly expensive to build, which limits their widespread adoption.
But I’m an optimist. I believe that these challenges are solvable. And I believe that the rewards are worth the effort. Humanoid robots have the potential to revolutionize every industry, from manufacturing and logistics to healthcare and entertainment. They can do the jobs that are too dangerous, too difficult, or too boring for humans. They can be our assistants, our companions, and our collaborators.
So, if you’re thinking about building a humanoid robot, my advice is this: go for it. It’s going to be a long and difficult journey, but it’s also going to be one of the most rewarding things you’ll ever do. And who knows, you might just build the next big thing.
Frequently Asked Questions
How do I measure success with this approach?
Pick one or two metrics that directly tie to your goal and track them weekly. Vanity metrics like page views or follower counts rarely matter. Focus on metrics that reflect real engagement or revenue impact.
What tools do I need to get started?
Start with the basics. You don't need expensive software or fancy tools. A spreadsheet, a note-taking app, and direct access to your customers will get you further than any enterprise platform. Add tools only when you hit a specific bottleneck.
What are the most common mistakes when building a humanoid robot (the guide i wish i had)?
The biggest mistake I see is overcomplicating things early on. Start with the simplest version that works, get real feedback, and iterate from there. Another common trap is copying what worked for someone else without understanding the context behind their decisions.