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Berry pickup from Kyber Labs Robot hands that can pick up fragile objects quickly will be important to solving long-tail of robotics problems not currently automated

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Chris Paxton

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33,336 просмотров • 1 месяц назад •via X (Twitter)

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We’re solving problems that aren’t problems in America This is the new automated ketchup dispenser from Server Products at Raising Canes I think there is a bigger plan with these new dispensers, and they’re being rolled out at multiple chains. These dispensers are IoT-enabled in newer models, meaning they can monitoring usage and inventory This kind of tracking and quickly turn into new features like restricting the amount of condiments one user can get, similar to what’s being done with soda machines and QR codes It starts as convenience, and ends with restrictions and control
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You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI
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It's the little things that make me excited about the future. Many people are (rightfully) worried that AI and robotics will take their jobs. Yes, it's about money they need to live on, and solutions are needed. At the same time, it's often work that's strenuous and tedious. So we can be glad when robots take these tasks off our hands. Furthermore, robots don't need breaks, vacations, or sleep. They can be scaled quickly and are efficient. Globalization, and humanity as a whole, can therefore benefit massively from this increase in efficiency.

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Kitten update #25: We're starting to let the kittens explore more of the house. The soda cases are blocking the door gap to keep them from escaping, but it's also provided a convenient barrier that Buffy respects so that the door can be open and the kitties can decide whether to engage with her or not. So far, they have enthusiastically chosen YES. Fortunately, Buffy has realized that the face end tastes better than the tail end because it might have some cat food on it. Besides the dog time, they also got to have new experiences with wood floors (soooo slippery!), stairs (fun to leap on), and the robot vacuum (not really that scary but not really making friends either). I suspect the afternoon nap will be extra intense today.

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AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

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