Since ChatGPT was released, we now interact with AI tools more directly—and regularly—than ever before. But interacting with robots, by way of contrast, is still a rarity for most. If you don’t undergo complex surgery or work in logistics, the most advanced robot you encounter in your daily life might still be a vacuum cleaner (if you’re feeling young, the first Roomba was released 22 years ago). But that’s on the cusp of changing. Roboticists believe that by using new AI techniques, they will achieve something the field has pined after for decades: more capable robots that can move freely through unfamiliar environments and tackle challenges they’ve never seen before. “It’s like being strapped to the front of a rocket,” says Russ Tedrake, vice president of robotics research at the Toyota Research Institute, says of the field’s pace right now. Tedrake says he has seen plenty of hype cycles rise and fall, but none like this one. “I’ve been in the field for 20-some years. This is different,” he says. But something is slowing that rocket down: lack of access to the types of data used to train robots so they can interact more smoothly with the physical world. It’s far harder to come by than the data used to train the most advanced AI models like GPT—mostly text, images, and videos scraped off the internet. Simulation programs can help robots learn how to interact with places and objects, but the results still tend to fall prey to what’s known as the “sim-to-real gap,” or failures that arise when robots move from the simulation to the real world. For now, we still need access to physical, real-world data to train robots. That data is relatively scarce and tends to require a lot more time, effort, and expensive equipment to collect. That scarcity is one of the main things currently holding progress in robotics back.
Full commentary : AI is upending the way robotics/humanoids are being developed, leaving companies and researchers with a need for more training.
