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…

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

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.

...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.

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.