The New Age of Robotics: How AI is Revolutionizing the Field

Tuesday - April 30 2024, 16:01 UTC - 9 months ago

The field of robotics is advancing rapidly thanks to new AI techniques. However, access to physical data is a major obstacle in their progress. Companies and labs are finding unconventional ways to gather this data, but they are also encountering similar issues as those in the chatbot world. Despite this setback, robots can now learn through data, similar to how language models learn from novels.

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.As a result, leading companies and labs are in fierce competition to find new and better ways to gather the data they need. It’s led them down strange paths, like using robotic arms to flip pancakes for hours on end, watching thousands of hours of graphic surgery videos pulled from YouTube, or deploying researchers to numerous Airbnbs in order to film every nook and cranny. Along the way, they’re running into the same sorts of privacy, ethics, and copyright issues as their counterparts in the world of chatbots.

The new need for data .

For decades, robots were trained on specific tasks, like picking up a tennis ball or doing a somersault. While humans learn about the physical world through observation and trial and error, many robots were learning through equations and code. This method was slow, but even worse, it meant that robots couldn’t transfer skills from one task to a new one.

But now, AI advances are fast-tracking a shift that had already begun: letting robots teach themselves through data. Just as a language model can learn from a library’s worth of novels, robot models can be shown a few hundred demonstrations of a person washing ketchup off a plate using robotic grippers, for example, and then imitate the task without being taught explicitly what ketchup looks like or how to turn on the faucet.