Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Announcing the first production robot navigation framework on $500 hardware Explore the world once → your robot agent will relocalize and build a persistant, spatial memory across sessions SLAM, relocalization, loop closure, map i/o, planning, control No ROS. Open source.

stash_pomichter's profile picture

stash

8,661 subscribers

57,099 görüntüleme • 2 ay önce •via X (Twitter)

OyunEğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Open Source Robot Company K-Scale Labs has announced a ~$9,000 personal robot. The entire robotics open source system: hardware, software, and machine learning infrastructure, all tightly integrated and ready for your use case. I am placing an order. I will also order Optimus.
0:50

Open Source Robot Company K-Scale Labs has announced a ~$9,000 personal robot. The entire robotics open source system: hardware, software, and machine learning infrastructure, all tightly integrated and ready for your use case. I am placing an order. I will also order Optimus.

Brian Roemmele

173,836 görüntüleme • 1 yıl önce

SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software
3:35

SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software

Vaishnavi

19,745 görüntüleme • 4 ay önce

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot *and* a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.
2:26

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot *and* a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.

kwindla

49,032 görüntüleme • 7 ay önce

Introducing MARS, the first Personal AI Robot. In technology, getting to the future we want starts with building it together. This is why we designed Mars. Inspired by early PCs, Mars is a powerful, complete, extendable robot. It runs BASIC, an open embodied AI agent we created for you to program with code, demonstrations, and prompts. This allows Mars to perform complex long-horizon tasks involving spatial memory, reasoning, manipulation and navigation. Pre-orders open now ⬇️ Demos & details in the thread
1:10

Introducing MARS, the first Personal AI Robot. In technology, getting to the future we want starts with building it together. This is why we designed Mars. Inspired by early PCs, Mars is a powerful, complete, extendable robot. It runs BASIC, an open embodied AI agent we created for you to program with code, demonstrations, and prompts. This allows Mars to perform complex long-horizon tasks involving spatial memory, reasoning, manipulation and navigation. Pre-orders open now ⬇️ Demos & details in the thread

Axel

314,180 görüntüleme • 1 yıl önce

We don’t generate videos. 🎬 We generate worlds from videos. 🌍 Introducing InSpatio-World — the world's first open-source real-time 4D world model‼️ Your input: a video clip Our output: a dynamic, navigable, persistent world 🕹️ explore freely across viewpoints ⏪ control time forward and backward 🔓 open-source and ready to build on :) Live demo: 🔗 Code & weights: 🔗 Project page: 🔗
1:32

We don’t generate videos. 🎬 We generate worlds from videos. 🌍 Introducing InSpatio-World — the world's first open-source real-time 4D world model‼️ Your input: a video clip Our output: a dynamic, navigable, persistent world 🕹️ explore freely across viewpoints ⏪ control time forward and backward 🔓 open-source and ready to build on :) Live demo: 🔗 Code & weights: 🔗 Project page: 🔗

InSpatio｜影溯

110,007 görüntüleme • 5 ay önce

Booster Robotics has launched Booster Studio, the first IDE built specifically for embodied AI. > Booster Studio features code editing, high-precision simulation, real-robot debugging, and real-world deployment. > It shortens the path from the first idea to the working hardware, with the build, test, and deployment loop living in a single environment rather than scattered across separate tools. Vibe Robotics 👀
1:21

Booster Robotics has launched Booster Studio, the first IDE built specifically for embodied AI. > Booster Studio features code editing, high-precision simulation, real-robot debugging, and real-world deployment. > It shortens the path from the first idea to the working hardware, with the build, test, and deployment loop living in a single environment rather than scattered across separate tools. Vibe Robotics 👀

🚨 AI News | TestingCatalog

21,276 görüntüleme • 2 ay önce

Anthropic engineer: "Stop re-prompting Claude all day. Build the loop once. Let it do the work." 00:00 - Build your first autonomous agent 05:02 - Make the loop fix its own mistakes 16:43 - Give Claude memory across sessions Prompting is the old workflow. Loops are the new one. This 30-minute watch is worth more than most paid agent courses. Bookmark and watch it today Then read the step-by-step guide below
31:36

Anthropic engineer: "Stop re-prompting Claude all day. Build the loop once. Let it do the work." 00:00 - Build your first autonomous agent 05:02 - Make the loop fix its own mistakes 16:43 - Give Claude memory across sessions Prompting is the old workflow. Loops are the new one. This 30-minute watch is worth more than most paid agent courses. Bookmark and watch it today Then read the step-by-step guide below

Lunar

318,397 görüntüleme • 1 ay önce

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:20

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,457 görüntüleme • 3 ay önce

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
1:37

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

161,260 görüntüleme • 5 ay önce

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇
1:08

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 görüntüleme • 19 gün önce

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 görüntüleme • 4 ay önce

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.
1:05

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

CyrilXBT

34,607 görüntüleme • 3 ay önce

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗴𝗼 𝗳𝗿𝗼𝗺 𝘁𝗲𝗹𝗲𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮 𝗿𝗼𝗯𝗼𝘁 𝘁𝗼 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗮 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗔𝗜 𝗽𝗼𝗹𝗶𝗰𝘆 𝗶𝗻 𝗹𝗲𝘀𝘀 𝘁𝗶𝗺𝗲 𝘁𝗵𝗮𝗻 𝗶𝘁 𝘁𝗮𝗸𝗲𝘀 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 𝗮 𝗺𝗼𝘃𝗶𝗲? Today, we're excited to open source our complete example showing exactly that: using Neuracore with the Piper robot from AgileX Robotics. What’s included: • 𝗠𝗲𝘁𝗮 𝗤𝘂𝗲𝘀𝘁 𝘁𝗲𝗹𝗲𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Control your robot with VR • 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 – Demo collection to model training to policy deployment • 𝗥𝗢𝗦 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝗿 – See your headset data with a single command • 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗲𝘁𝗮 𝗤𝘂𝗲𝘀𝘁 𝘁𝗲𝗹𝗲𝗼𝗽 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 – Works with any ROS-enabled robot If you have a Piper robot, grab the example, sign up for Neuracore, and show us what you build. Repo: Learn more: Open Robotics Meta Quest
0:44

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗴𝗼 𝗳𝗿𝗼𝗺 𝘁𝗲𝗹𝗲𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮 𝗿𝗼𝗯𝗼𝘁 𝘁𝗼 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗮 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗔𝗜 𝗽𝗼𝗹𝗶𝗰𝘆 𝗶𝗻 𝗹𝗲𝘀𝘀 𝘁𝗶𝗺𝗲 𝘁𝗵𝗮𝗻 𝗶𝘁 𝘁𝗮𝗸𝗲𝘀 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵 𝗮 𝗺𝗼𝘃𝗶𝗲? Today, we're excited to open source our complete example showing exactly that: using Neuracore with the Piper robot from AgileX Robotics. What’s included: • 𝗠𝗲𝘁𝗮 𝗤𝘂𝗲𝘀𝘁 𝘁𝗲𝗹𝗲𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Control your robot with VR • 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 – Demo collection to model training to policy deployment • 𝗥𝗢𝗦 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝗿 – See your headset data with a single command • 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗲𝘁𝗮 𝗤𝘂𝗲𝘀𝘁 𝘁𝗲𝗹𝗲𝗼𝗽 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 – Works with any ROS-enabled robot If you have a Piper robot, grab the example, sign up for Neuracore, and show us what you build. Repo: Learn more: Open Robotics Meta Quest

Neuracore

21,894 görüntüleme • 7 ay önce

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning *inside* a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation *inside* a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:
1:17

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning *inside* a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation *inside* a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

Jim Fan

227,634 görüntüleme • 6 ay önce

QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.
0:58

QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.

Qace Dynamics

12,654 görüntüleme • 9 ay önce

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.
0:20

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Dhruv Diddi

31,751 görüntüleme • 4 ay önce

This is a great time to learn #robotics! Robot AI has advanced to where a novice like myself can build / train this folding robot in my garage. The tech is not perfect, but it is improving *very quickly* (exciting new papers daily), and will likely be ubiquitous within 2-3 years. To learn more, great resources are: LeRobot Remi Cadene (open-source software), Alexander Koch , Rob Knight (open-source hardware), Chris Paxton, Stone Tao (news + papers). Also, 50 other grad students that I missed.
1:00

This is a great time to learn #robotics! Robot AI has advanced to where a novice like myself can build / train this folding robot in my garage. The tech is not perfect, but it is improving *very quickly* (exciting new papers daily), and will likely be ubiquitous within 2-3 years. To learn more, great resources are: LeRobot Remi Cadene (open-source software), Alexander Koch , Rob Knight (open-source hardware), Chris Paxton, Stone Tao (news + papers). Also, 50 other grad students that I missed.

pfung

45,791 görüntüleme • 1 yıl önce

1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n
1:48

1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n

Zhi (Leo) Wang

51,729 görüntüleme • 2 ay önce

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing
1:03

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing

lagerskoy

57,099 görüntüleme • 2 ay önce