robotics and AI

...The awe-inspiring Carbon Robotics LaserWeeder G2 packs in 24 high power lasers and 24 Nvidia GPUs to facilitate its claimed “10,000 weeds per minute” zapping performance...The robotics device is also a massive labor saver, outperforming “a hand crew of 75 people,”...

A lot of the stuff we use today is largely made by robots—arms with multiple degrees of freedom positioned along conveyor belts that move in a spectacle of precisely synchronized motions. All this motion is usually programmed by hand, which can take hundreds to thousands of hours. Google’s DeepMind team has developed an AI system called RoboBallet that lets manufacturing robots figure out what to do on their own...

...“Because it works so fast, it would be possible for a designer to try different layouts and different placement or selections of robots in almost real time,” Lai says. This way, engineers at factories would be able to see exactly how much time they would save by adding another robot to a cell or choosing a robot of a different type. Another thing RoboBallet can do is reprogram the work cell on the fly, allowing other robots to fill in when one of them breaks down.

...per the relevant research results published in IEEE Transactions on Robotics, the hand exoskeleton is based on "magnetorheological (MR) actuators to provide high gripping support and elevate grip endurance." And it adopts a very different solution from most existing hand exoskeletons, which are "motorized, being weak in generating high supporting force for gripping augmentation,"...

...the research team also conducted simulated earthquake rescue...In the task of transporting the injured, compared to when they did not wear the exoskeleton gloves, the subjects' respiratory rate decreased by an average of 20 percent, while the maximum transport distance increased by 110 percent...

Participants wearing the hand exoskeletons experienced a 41.8 percent enhancement in grip strength without external power...

...MicroFactory highlighted that to set up the system, users must use copies of these robotic arms from outside the box.

Using another external robotic arm, users will show the in-box AI system how to perform a task. They can show how to grab an object and place it in a different place.

The original in-box robotic arms will copy the action and be able to perform the same tasks perfectly.

The company also made a UI that breaks down tasks into small steps.

“General purpose robots are good, but it’s not necessary [to] be humanoid,” said Igor Kulakov, the co-founder and CEO of MicroFactory...

It took them about five months to build their prototype. Now the company has hundreds of preorders from customers looking to use the machines for various applications, including assembling electronics and even processing snails to be shipped to France for escargot...

Fuel cells are energy solutions that can convert the chemical energy in fuels into electricity via specific chemical reactions, instead of relying on combustion. Promising types of fuel cells are direct methanol fuel cells (DMFCs), devices specifically designed to convert the energy in methyl alcohol (i.e., methanol) into electrical energy.

Despite their potential for powering large electronics, vehicles and other systems requiring portable power, these methanol-based fuel cells still have significant limitations. Most notably, studies found that their performance tends to significantly degrade over time, because the materials used to catalyze reactions in the cells (i.e., electrocatalytic surfaces) gradually become less effective.

One approach to cleaning these surfaces and preventing the accumulation of poisoning products produced during chemical reactions entails the modulation of the voltage applied to the fuel cells. However, manually adjusting the voltage applied to the surfaces in effective ways, while also accounting for physical and chemical processes in the fuel cells, is impractical for real-world applications.

Researchers at the Massachusetts Institute of Technology (MIT) recently developed Alpha-Fuel-Cell, a new machine learning-based tool that can monitor the state of a catalyst and adjust the voltage applied to it accordingly. The new computational tool, outlined in a paper published in Nature Energy, was found to improve the average power produced by direct methanol fuel cells by 153% compared to conventional manual voltage operation strategies...

..."This system is the first demonstration of a combination of AI and energy devices, maintaining maximum fuel cell power with automatic catalyst self-healing. The system figures out when short rests actually help the cell to recover, instead of wasting time . . . We're now scaling our approach from a single lab cell to larger, real-world stacks, adding safety and lifetime limits directly into the controller, and testing the same idea on batteries and other electrochemical systems to generalize it," added [Ju Li, senior author of the paper].

...The interface is in the form of a handheld, sensor-equipped tool that can attach to many common collaborative robotic arms. A person can use the attachment to teach a robot to carry out a task by remotely controlling the robot, physically manipulating it, or demonstrating the task themselves...

Conventional robots can easily be modeled as rigid links connected by joints, but it remains an open challenge to model and control biologically inspired robots that are often soft or made of several materials, lack sensing capabilities and may change their material properties with use. Here, we introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field (the sensitivity of all 3D points to the robot’s actuators). Our method enables the control of robots from only a single camera, makes no assumptions about the robots’ materials, actuation or sensing, and is trained without expert intervention by observing the execution of random commands...Because it enables robot control using a generic camera as the only sensor, we anticipate that our work will broaden the design space of robotic systems and serve as a starting point for lowering the barrier to robotic automation.

I miss Lego Mindstorms.

Interesting research from Anthropic. I'm looking forward to reading follow-on work, and I really hope that this will be tested on open source models (like Mistral) to confirm the method.

GitHub: https://github.com/arplaboratory/learning-to-fly