Future Timeline

With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down.

Reprogramming that robot involves hand-labeling thousands of images that show it how to grasp these new mugs, then training the system all over again.

But a new technique developed by MIT researchers would require only a handful of human demonstrations to reprogram the robot. This machine-learning method enables a robot to pick up and place never-before-seen objects that are in random poses it has never encountered. Within 10 to 15 minutes, the robot would be ready to perform a new pick-and-place task.

The technique uses a neural network specifically designed to reconstruct the shapes of 3D objects. With just a few demonstrations, the system uses what the neural network has learned about 3D geometry to grasp new objects that are similar to those in the demos.

You might have heard about the ancient Roman city of Pompeii which was struck by a catastrophy around 2000 years ago when the vulcan Vesuv erupted and covered the city with ash, killing everyone in the city. But what is no longer ancient in the city is their new guard dog.

For decades, researchers worldwide have been trying to develop robots that can efficiently assist humans and work alongside them as they tackle a variety of everyday tasks. To do this effectively, however, the robots should be able to interact naturally with humans, including handing them and receiving objects from them.

Researchers at NVIDIA have recently developed a model that could be used to enhance the ability of robots to naturally pass and receive objects from human agents. This approach, introduced in a paper set to be presented at ICRA 2022, is based on a framework called STORM, which they presented in one of their previous works.

"In this work, we focus specifically on making sure that robots can take any object from a human user," Dieter Fox, one of the researchers who carried out the study, told TechXplore. "We came up with a solution that integrates a learned approach for grasping with our predictive control approach, in order to ensure these handovers are predictable, natural, and fast, so that people see the robot as a safe, helpful assistant."

True general intelligence requires models that can not only read and write, but act in a way that is helpful to users. That’s why we’re starting Adept: we’re training a neural network to use every software tool and API in the world, building on the vast amount of existing capabilities that people have already created.

Yuli Ban wrote: ↑Tue Apr 26, 2022 9:08 pm
starspawn0

True general intelligence requires models that can not only read and write, but act in a way that is helpful to users. That’s why we’re starting Adept: we’re training a neural network to use every software tool and API in the world, building on the vast amount of existing capabilities that people have already created.

This is the gigantic remote-controlled robot drafted in to help fix railways in Japan.

Appearing like something straight out of a sci-fi movie, the Gundam-like creation is mounted on a crane and controlled virtually by an engineer.

The robot is able to carry out maintenance work on hard-to-reach railway contact lines.

In the quest to build smart skin that mimics the sensing capabilities of natural skin, ionic skins have shown significant advantages. They're made of flexible, biocompatible hydrogels that use ions to carry an electrical charge. In contrast to smart skins made of plastics and metals, the hydrogels have the softness of natural skin. This offers a more natural feel to the prosthetic arm or robot hand they are mounted on, and makes them comfortable to wear.

These hydrogels can generate voltages when touched, but scientists did not clearly understand how—until a team of researchers at UBC devised a unique experiment, published today in Science.

"How hydrogel sensors work is they produce voltages and currents in reaction to stimuli, such as pressure or touch—what we are calling a piezoionic effect. But we didn't know exactly how these voltages are produced," said the study's lead author Yuta Dobashi, who started the work as part of his master's in biomedical engineering at UBC.