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We just did the largest live deployment of interactive robots in history. Users online could ask the robot to do things it was never taught to do. A true foundation model, with 0 task-specific data and 0 task fine tuning. 1/10

Enigma

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203,408 次观看 • 1 个月前 •via X (Twitter)

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You got your HANDO ID. Now put it to work. Early access is live. Pick your first task, record it and earn stock tokens. The everyday things you know how to do are things robots still need to learn. Start your first task →

You got your HANDO ID. Now put it to work. Early access is live. Pick your first task, record it and earn stock tokens. The everyday things you know how to do are things robots still need to learn. Start your first task →

HANDO Labs

220,108 次观看 • 4 天前

We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: Code: Biological systems are capable of rapid adaptation, given limited sensory cues. For example, our human visual system can quickly adapt and tune its light sensitivity to our surroundings. While modern LLMs exhibit a wide variety of capabilities and knowledge, they remain rigid when adding task-specific capabilities. Traditionally, customizing these models requires gathering large datasets and performing often expensive, time-consuming fine-tuning for specific applications. To bypass these limitations, Text-to-LoRA (T2L) meta-learns a “hypernetwork” that takes in a text description of a desired task, as a prompt, and generates a task-specific LoRA that performs well on the task. In our experiments, we show that T2L can encode hundreds of existing LoRA adapters. While the compression is lossy, T2L maintains the performance of task-specifically tuned LoRA adapters. We also show that T2L can even generalize to unseen tasks given a natural language description of the tasks. Importantly, Text-to-LoRA is parameter-efficient. It generates LoRAs in a single, inexpensive step, based solely on a simple text description of the task. This approach is a step towards dramatically lowering the technical and computational barriers, allowing non-technical users to specialize foundation models using plain language, rather than needing deep technical expertise or large compute resources.

We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: Code: Biological systems are capable of rapid adaptation, given limited sensory cues. For example, our human visual system can quickly adapt and tune its light sensitivity to our surroundings. While modern LLMs exhibit a wide variety of capabilities and knowledge, they remain rigid when adding task-specific capabilities. Traditionally, customizing these models requires gathering large datasets and performing often expensive, time-consuming fine-tuning for specific applications. To bypass these limitations, Text-to-LoRA (T2L) meta-learns a “hypernetwork” that takes in a text description of a desired task, as a prompt, and generates a task-specific LoRA that performs well on the task. In our experiments, we show that T2L can encode hundreds of existing LoRA adapters. While the compression is lossy, T2L maintains the performance of task-specifically tuned LoRA adapters. We also show that T2L can even generalize to unseen tasks given a natural language description of the tasks. Importantly, Text-to-LoRA is parameter-efficient. It generates LoRAs in a single, inexpensive step, based solely on a simple text description of the task. This approach is a step towards dramatically lowering the technical and computational barriers, allowing non-technical users to specialize foundation models using plain language, rather than needing deep technical expertise or large compute resources.

Sakana AI

403,396 次观看 • 1 年前

MAKING AXIS TASKS FEEL EASY 👀🤖 I made a quick video showing how to complete an Axis Robotics task step by step. The task gives you a clear goal, steps, controls and camera views to guide you. In this one, the goal is simple, Open the trash bin, pick up the onion, and place it inside. 🧅 Just follow the instructions, control the robot arm and complete the task. What I like is that even beginners can understand what they need to do. But the interesting part is what happens with these interactions after we complete them. Each completed task can contribute valuable robot training data, helping Physical Ai systems learn how to handle different objects and environments. Small tasks from the community can become useful data for teaching robots how to interact with the real world. That’s what makes Axis interesting to me.

MAKING AXIS TASKS FEEL EASY 👀🤖 I made a quick video showing how to complete an Axis Robotics task step by step. The task gives you a clear goal, steps, controls and camera views to guide you. In this one, the goal is simple, Open the trash bin, pick up the onion, and place it inside. 🧅 Just follow the instructions, control the robot arm and complete the task. What I like is that even beginners can understand what they need to do. But the interesting part is what happens with these interactions after we complete them. Each completed task can contribute valuable robot training data, helping Physical Ai systems learn how to handle different objects and environments. Small tasks from the community can become useful data for teaching robots how to interact with the real world. That’s what makes Axis interesting to me.

SufianXFN

14,419 次观看 • 26 天前

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

The Daily Ai

33,341 次观看 • 29 天前

How can a visuomotor policy learn from internet videos? We introduce Dreamitate, where a robot uses a fine-tuned video diffusion model to dream the future (top) and imitate the dream to accomplish a task (bottom). website: paper:

How can a visuomotor policy learn from internet videos? We introduce Dreamitate, where a robot uses a fine-tuned video diffusion model to dream the future (top) and imitate the dream to accomplish a task (bottom). website: paper:

Ruoshi Liu

50,844 次观看 • 2 年前

In a new room, we connected GPT-6 Astra to a robot. Simply telling it to turn the knob to start the washing machine was enough for it to complete the task.🤯

In a new room, we connected GPT-6 Astra to a robot. Simply telling it to turn the knob to start the washing machine was enough for it to complete the task.🤯

ARX

218,387 次观看 • 26 天前

Super excited about Hydra-0 from Hongyu Li and team! The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.

Super excited about Hydra-0 from Hongyu Li and team! The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.

Yunzhu Li

10,850 次观看 • 1 个月前

Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for 👀 > Time to complete the task > Tokens spent > Quality of the final result (dashboard in browser)

Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for 👀 > Time to complete the task > Tokens spent > Quality of the final result (dashboard in browser)

🚨 AI News | TestingCatalog

142,411 次观看 • 4 个月前

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Alright, now that we know what an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 次观看 • 10 个月前

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,389 次观看 • 1 个月前

What if your model handled its own tuning? ⚙️ With NVIDIA TAO 7, just prompt your coding agent what you want in plain language and let it do the rest. • Agent skills plug into your coding agent and improve accuracy. • AutoML removes the guesswork from hyperparameters. LLM-guided tuning finds strong configs up to 2x faster. • Fine-tune any Hugging Face CV or VLM model on local NVIDIA GPUs. • Data enhanced fine-tuning helps your agent identify why the model fails, then fix it. Try TAO skills ➡️

What if your model handled its own tuning? ⚙️ With NVIDIA TAO 7, just prompt your coding agent what you want in plain language and let it do the rest. • Agent skills plug into your coding agent and improve accuracy. • AutoML removes the guesswork from hyperparameters. LLM-guided tuning finds strong configs up to 2x faster. • Fine-tune any Hugging Face CV or VLM model on local NVIDIA GPUs. • Data enhanced fine-tuning helps your agent identify why the model fails, then fix it. Try TAO skills ➡️

NVIDIA AI

42,294 次观看 • 3 个月前

Most AI in consumer apps is a call to someone else’s cloud. Meanwhile the very capable computer already in your hand sits idle. So we gave it a job! To-dos rarely start inside a to-do app. They start as a thought you say out loud, a note in your notebook, or something scribbled on a whiteboard. Introducing 2 on-device AI features: 🎙️1. Yap with Uncle Rudy Yap isn’t just voice input. Behind it is a product-specific SLM we trained, specifically trained for one job: understanding task operations - add, update, delete, and reschedule. Yap → Tell Rudy what to do → Rudy turns it into the right task operation. 📸2. Snap with Uncle Rudy The same idea applies to the things that never made it into an app. Snap uses Apple’s new Foundation Vision Model to understand a handwritten note, whiteboard, or notebook page and turn it into structured tasks - entirely on-device. Talk to Rudy. Point at a page. Meet your to-dos where they actually begin. Uncle Rudy - One of our first experiments in personal on-device AI. TestFlight Dropping Tomorrow!

Most AI in consumer apps is a call to someone else’s cloud. Meanwhile the very capable computer already in your hand sits idle. So we gave it a job! To-dos rarely start inside a to-do app. They start as a thought you say out loud, a note in your notebook, or something scribbled on a whiteboard. Introducing 2 on-device AI features: 🎙️1. Yap with Uncle Rudy Yap isn’t just voice input. Behind it is a product-specific SLM we trained, specifically trained for one job: understanding task operations - add, update, delete, and reschedule. Yap → Tell Rudy what to do → Rudy turns it into the right task operation. 📸2. Snap with Uncle Rudy The same idea applies to the things that never made it into an app. Snap uses Apple’s new Foundation Vision Model to understand a handwritten note, whiteboard, or notebook page and turn it into structured tasks - entirely on-device. Talk to Rudy. Point at a page. Meet your to-dos where they actually begin. Uncle Rudy - One of our first experiments in personal on-device AI. TestFlight Dropping Tomorrow!

Conscious Engines • Studio

11,362 次观看 • 1 个月前

GPT-5.1 is now live in Warp. It’s much faster (40% faster task completion on a subset of SWE-bench Verified) without compromising quality. GPT-5.1 is available to all Warp users, and is now the default model for all new users.

GPT-5.1 is now live in Warp. It’s much faster (40% faster task completion on a subset of SWE-bench Verified) without compromising quality. GPT-5.1 is available to all Warp users, and is now the default model for all new users.

Warp

80,683 次观看 • 10 个月前

Peeling a potato is trivial -- until you try to make a robot do it with a knife. This is actually one of the hardest problems in manipulation: contact-rich, force-sensitive, and success is subjective. We taught a robot arm to peel with >90% success :D

Peeling a potato is trivial -- until you try to make a robot do it with a knife. This is actually one of the hardest problems in manipulation: contact-rich, force-sensitive, and success is subjective. We taught a robot arm to peel with >90% success :D

Toru

33,660 次观看 • 7 个月前

We were able to embed all of Wikipedia in < 15 minutes on Modal Labs for ~ 17$. Here's what this actually means for an organization: 1. Unrestricted Rates: Eliminates the bottleneck of rate limits for large-scale operations. 2. Rapid Experimentation: Allows quick iterations of data chunking experiments, reducing wait times from hours to minutes. 3. Fine-tuning: Use of the same hf infrastructure for model fine-tuning, speeding up experiment cycles, and deployment. Reducing experiment timelines from hours to approximately to minutes is something that has compounding effects! take a look!

We were able to embed all of Wikipedia in < 15 minutes on Modal Labs for ~ 17$. Here's what this actually means for an organization: 1. Unrestricted Rates: Eliminates the bottleneck of rate limits for large-scale operations. 2. Rapid Experimentation: Allows quick iterations of data chunking experiments, reducing wait times from hours to minutes. 3. Fine-tuning: Use of the same hf infrastructure for model fine-tuning, speeding up experiment cycles, and deployment. Reducing experiment timelines from hours to approximately to minutes is something that has compounding effects! take a look!

jason

230,419 次观看 • 2 年前

The team just sent me this funny video of how AWFUL the days after the 2020 election were. People were kinda laughing at me, but it was miserable. And little did we know how bad it would truly get under the Biden and progressive retaliation machine. This was when we first locked in with the Charlie Kirk show and kept up real time updates and analysis where Arizona and a number of other states became central. I never want to go back to this feeling. Never. Charlie was adamant that we do everything we could possibly do to fix this in 2024. We DID IT and we must continue for 2026 and 2028 to have a shot at saving this country.

The team just sent me this funny video of how AWFUL the days after the 2020 election were. People were kinda laughing at me, but it was miserable. And little did we know how bad it would truly get under the Biden and progressive retaliation machine. This was when we first locked in with the Charlie Kirk show and kept up real time updates and analysis where Arizona and a number of other states became central. I never want to go back to this feeling. Never. Charlie was adamant that we do everything we could possibly do to fix this in 2024. We DID IT and we must continue for 2026 and 2028 to have a shot at saving this country.

Tyler Bowyer

25,672 次观看 • 4 个月前

The interactive product page is now fully live on Shopify . Tested the integration of interactive animation directly within the online store. You can configure the product, and all data stays in sync through to checkout. 🎄 Test it:

The interactive product page is now fully live on Shopify . Tested the integration of interactive animation directly within the online store. You can configure the product, and all data stays in sync through to checkout. 🎄 Test it:

Daria Nevezhyna

292,080 次观看 • 9 个月前

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

LeRobot

25,163 次观看 • 4 个月前