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🛠️ What if a robot could invent its own tools. And teach itself how to use them? That’s exactly what VLMgineer does: a new framework that lets Vision Language Models (VLMs) design physical tools and the actions to use them, entirely on their own. No templates. No human demonstrations....

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Ilir Aliu

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13,984 просмотров • 8 месяцев назад •via X (Twitter)

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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 просмотров • 9 месяцев назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3
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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 8 месяцев назад

🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.
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🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.

Bernard Marr

10,980 просмотров • 1 год назад

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:
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Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

38,234 просмотров • 6 месяцев назад

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓
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WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓

SCOTTY BEAM

4,813,581 просмотров • 23 дней назад

Most imitation learning policies break when the camera moves or the robot changes. NOT THIS ONE 👇 [📍 Bookmark for later ] A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints… And it works with any IL algorithm. The trick? •Use a 2D foundation model to extract semantic features •Lift them into 3D space for localization (not semantics) •Condition the IL policy on this spatially grounded vector Across 93 simulated and 6 real tasks, Adapt3R: ✅ Maintains IL performance on LIBERO & MimicGen benchmarks ✅ Outperforms DP3 and 3D Diffuser Actor in most settings ✅ Holds >80% success on LIBERO even with large camera rotations Thanks for sharing this, Animesh Garg & Albert Wilcox! 📍Paper: Website: Code:
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Most imitation learning policies break when the camera moves or the robot changes. NOT THIS ONE 👇 [📍 Bookmark for later ] A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints… And it works with any IL algorithm. The trick? •Use a 2D foundation model to extract semantic features •Lift them into 3D space for localization (not semantics) •Condition the IL policy on this spatially grounded vector Across 93 simulated and 6 real tasks, Adapt3R: ✅ Maintains IL performance on LIBERO & MimicGen benchmarks ✅ Outperforms DP3 and 3D Diffuser Actor in most settings ✅ Holds >80% success on LIBERO even with large camera rotations Thanks for sharing this, Animesh Garg & Albert Wilcox! 📍Paper: Website: Code:

Ilir Aliu

12,178 просмотров • 1 год назад

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it
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TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 просмотров • 25 дней назад

Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: ✅ Hand eye calibration from plain RGB or RGB D images ✅ Only three robot poses needed for millimeter accuracy ✅ Works with any ROS 2 compatible robot and camera ✅ Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:
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Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: ✅ Hand eye calibration from plain RGB or RGB D images ✅ Only three robot poses needed for millimeter accuracy ✅ Works with any ROS 2 compatible robot and camera ✅ Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:

Ilir Aliu

18,406 просмотров • 9 месяцев назад

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2
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💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

Nihan

19,296 просмотров • 4 месяцев назад

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA
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Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 просмотров • 2 месяцев назад

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)
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Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Utkarsh Sharma

29,899 просмотров • 6 месяцев назад

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)
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Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Mike Futia

113,100 просмотров • 6 месяцев назад

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)
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Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Manoj Kumar Shah

17,146 просмотров • 6 месяцев назад

Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)
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Claude Cowork is f*cking ridiculous 🤯 One prompt → competitive research, creative briefs, 15 hook variations, and a full performance dashboard. All saved as real files on your computer. All inside Claude Desktop. If you're spending hours every week copy-pasting between tools, pulling competitor ads manually, writing briefs from scratch, and building reports in spreadsheets ... Claude Cowork eliminates the entire loop: → Point it at your project folder with brand voice + context files → It asks YOU clarifying questions instead of guessing → It builds a multi-step plan and executes while you step away → It creates real .docx, .xlsx, .pptx files — not chat responses → It connects to Slack, Google Drive, Airtable, and 50+ tools live No copy-pasting between tools. No babysitting the AI mid-task. No downloading and re-uploading files. What you get: → Competitive research synthesized into actionable creative angles → Ad briefs, hooks, and scripts generated in your brand voice → Interactive HTML dashboards built from your own customer data → Weekly performance reports created while you're getting coffee Built 100% inside Claude Desktop with skills, plugins, and connectors. I put together a full DTC playbook: 10 workflows with copy-paste prompts, the exact setup process, and the weekly operating rhythm I use. Want it for free? > Like this post > Comment "COWORK" And I'll send it over (must be following so I can DM)

Abdul Sarfraj

30,854 просмотров • 6 месяцев назад

🚨 AI JUST DESIGNED A MATERIAL STRONGER THAN STEEL, LIGHTER THAN FOAM AND UP TO 5× STRONGER THAN TITANIUM. Researchers used machine learning to discover entirely new microscopic lattice structures that were then 3D-printed into carbon nanolattices. The result is a mechanical metamaterial that combines properties previously thought to be impossible together: extreme strength with ultra-low weight. Why this matters: • Aerospace and automotive industries could build dramatically lighter vehicles and aircraft without sacrificing strength • Construction and infrastructure could use stronger, lighter components • Medical implants and protective gear could become both tougher and more comfortable • It proves AI can now design physical matter at the structural level exploring geometries no human engineer would have thought of The deeper implication is huge: We are moving from discovering materials that already exist in nature… to inventing entirely new classes of matter with properties we specify. AI isn’t just writing code or generating images anymore. It’s helping us build the physical world from the inside out. What industry do you think will be transformed first by these AI-designed super-materials? Follow for more frontier science and future technology.
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🚨 AI JUST DESIGNED A MATERIAL STRONGER THAN STEEL, LIGHTER THAN FOAM AND UP TO 5× STRONGER THAN TITANIUM. Researchers used machine learning to discover entirely new microscopic lattice structures that were then 3D-printed into carbon nanolattices. The result is a mechanical metamaterial that combines properties previously thought to be impossible together: extreme strength with ultra-low weight. Why this matters: • Aerospace and automotive industries could build dramatically lighter vehicles and aircraft without sacrificing strength • Construction and infrastructure could use stronger, lighter components • Medical implants and protective gear could become both tougher and more comfortable • It proves AI can now design physical matter at the structural level exploring geometries no human engineer would have thought of The deeper implication is huge: We are moving from discovering materials that already exist in nature… to inventing entirely new classes of matter with properties we specify. AI isn’t just writing code or generating images anymore. It’s helping us build the physical world from the inside out. What industry do you think will be transformed first by these AI-designed super-materials? Follow for more frontier science and future technology.

TheNewPhysics

156,085 просмотров • 3 месяцев назад

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:
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Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 просмотров • 1 месяц назад