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Animal-like robots are the future. Introducing Verdie, a physical agent for sustainable outdoor maintenance capable of learning multiple power tools. Verdie was trained in the NVIDIA Isaac simulator for just 5 hours before string trimming lawns for the first time. Verdie is being deployed in 2024 to work alongside... show more

Electric Sheep

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27,052 görüntüleme • 2 yıl önce •via X (Twitter)

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Electric Sheep profil fotoğrafı

Electric Sheep2 yıl önce

Link to substack to read more about Verdie's AI :

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Pumped to see some great coverage for little Verdie @sheeprobotics alongside the amazing @Figure_robot and @adcock_brett funding announcement in NBC Bay Area yesterday.

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Mani2 yıl önce

@nvidia Verdie looks really cute, like Wall-E irl

Yeshua God - Official profil fotoğrafı

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@nvidia OMG when @the_yanco sees this wielding a chainsaw in his neighbor's yard he's gonna go nuts 🤣

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SGM2 yıl önce

@nvidia A bot that can walk/play fetch with the dog is still needed

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@nvidia @SimonNimes

PrimeURL (for Startups 🏆) profil fotoğrafı

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@nvidia Robot 🤖

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@drew_r_hamilton @nvidia thanks - we think so too!

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We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

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When firefighters are sent out in crews of just three in a fire engine, lives are put at risk. The fragmentation of the fire and rescue service has led to a dangerous post-code lottery for firefighter crews. To protect firefighters and the public, we need national standards.

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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

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52,110 görüntüleme • 9 ay önce

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Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

AK

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✨ Light doesn’t just brighten paths—it ignites possibilities. For over 60 years, Yutong has harnessed the power of innovation to drive the future of new energy. From intelligent mobility solutions to a global network of 1,900+ service centers, we’re engineering reliable, efficient, and sustainable transportation—for every community, and for our planet. From powering greener mobility to driving global transformation, we believe in the power of light to illuminate a better future.

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For the first time in our 111-year history, U.S. Soccer is launching a comprehensive fundraising campaign. Behind the Dream: The Campaign for the Future of Soccer in America will raise $250 million over five years and will fuel our mission to be In Service to Soccer. This is our moment to shape the future of soccer in America. Join us by visiting

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Your things are not really your things. The value, information, and power generated by our connected devices and machines are being concentrated in the hands of Big Tech. It doesn't have to be this way. peaq builds a future that doesn't just work for the 1%, but for the 100%.

Your things are not really your things. The value, information, and power generated by our connected devices and machines are being concentrated in the hands of Big Tech. It doesn't have to be this way. peaq builds a future that doesn't just work for the 1%, but for the 100%.

peaq

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Our subsidiary VARD has recently launched the CSOV NB 986 at Vard Shipyards Romania – Brăila, the sixth wind farm service vessel built for Norwind Offshore. The unit is based on the VARD 4 19 design and tailored for sustainable offshore wind operations, confirming our commitment to sustainable maritime solutions. Production work will now continue in Brăila before the vessel is towed to Vard Søviknes for final outfitting and completion. Together with VARD, we are not just shipbuilders, but the control room of an ecosystem that invests every day in innovation and sustainability, building the future of the blue economy.

Our subsidiary VARD has recently launched the CSOV NB 986 at Vard Shipyards Romania – Brăila, the sixth wind farm service vessel built for Norwind Offshore. The unit is based on the VARD 4 19 design and tailored for sustainable offshore wind operations, confirming our commitment to sustainable maritime solutions. Production work will now continue in Brăila before the vessel is towed to Vard Søviknes for final outfitting and completion. Together with VARD, we are not just shipbuilders, but the control room of an ecosystem that invests every day in innovation and sustainability, building the future of the blue economy.

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