In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries. There are also full-sequence diffusion models like Sora, which convert words into dazzling, realistic visuals by successively “denoising” an entire video sequence

Multimaterial 3D printing enables makers to fabricate customized devices with multiple colors and varied textures. But the process can be time-consuming and wasteful because existing 3D printers must switch between multiple nozzles, often discarding one material before they can start depositing another.

For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.

The phrase “practice makes perfect” is usually reserved for humans, but it’s also a great maxim for robots newly deployed in unfamiliar environments.

As a child, I often accompanied my mother to the grocery store. As she pulled out her card to pay, I heard the same phrase like clockwork: "Go bag the groceries." It was not my favorite task.

When robots come across unfamiliar objects, they struggle to account for a simple truth: Appearances aren’t everything. They may attempt to grasp a block, only to find out it’s a literal piece of cake. The misleading appearance of that object could lead the robot to miscalculate physical properties like the object’s weight and center of mass, using the wrong grasp and applying more force than needed.

Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.

Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.

The recent ransomware attack on ChangeHealthcare, which severed the network connecting health care providers, pharmacies, and hospitals with health insurance companies, demonstrates just how disruptive supply chain attacks can be. In this case, it hindered the ability of those providing medical services to submit insurance claims and receive payments.