正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

A first look at the Forma RaaS Control Interface in action. Live robot state visualization, joint-level control, and task orchestration for 6-DoF arms - all running through a single, web-native dashboard. This control layer is fundamental to Robotics-as-a-Service. It’s what turns robots into remotely accessible, observable, and controllable resources... show more

Forma Robotics

1,494 subscribers

40,274 次观看 • 8 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Built Copilot Mission Control to visualize what GitHub Copilot CLI is doing in real time.🌟 It shows: - Active sessions - Tool usage - Input/output tokens - Live activity streams - Replay/time travel - Turns - Full visualization of the entire process A second-monitor dashboard for agent work to look into skills, MCP tools, and more being used. Work in progress - more to come! 🚀

Built Copilot Mission Control to visualize what GitHub Copilot CLI is doing in real time.🌟 It shows: - Active sessions - Tool usage - Input/output tokens - Live activity streams - Replay/time travel - Turns - Full visualization of the entire process A second-monitor dashboard for agent work to look into skills, MCP tools, and more being used. Work in progress - more to come! 🚀

Dan Wahlin

17,405 次观看 • 3 个月前

Another robot from Disney! 🕺🏻 Just look at this dancing fella created by Disney Research. Creating control policies that work on real robots and handling diverse, unseen motions in physics-based character control is still difficult. The Disney team proposes a two-state solution. The first step involves learning a latent space encoding with an autoencoder. After encoding, the policy is trained to map kinematic input to dynamic output, ensuring accurate and adaptable movement. This method eliminates common mode collapse issues and improves motion encoding by separating these stages. This technique has proven effective in real robots in simulations, marking a significant leap forward in robot control. Can't wait for it in action live in 2025 :) ~~~ RT to help 1 robot find a new workplace.

Another robot from Disney! 🕺🏻 Just look at this dancing fella created by Disney Research. Creating control policies that work on real robots and handling diverse, unseen motions in physics-based character control is still difficult. The Disney team proposes a two-state solution. The first step involves learning a latent space encoding with an autoencoder. After encoding, the policy is trained to map kinematic input to dynamic output, ensuring accurate and adaptable movement. This method eliminates common mode collapse issues and improves motion encoding by separating these stages. This technique has proven effective in real robots in simulations, marking a significant leap forward in robot control. Can't wait for it in action live in 2025 :) ~~~ RT to help 1 robot find a new workplace.

Lukas Ziegler

65,972 次观看 • 1 年前

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,898 次观看 • 7 个月前

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 | 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 次观看 • 9 个月前

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Megatron

43,247 次观看 • 3 个月前

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

Zhengyi “Zen” Luo

62,515 次观看 • 10 个月前

A humanoid robot playing a Yamaha drum kit is one of the cleanest ways to test rhythm control in public. Drumming exposes timing, wrist motion, impact force, arm recovery, and coordination between both hands. Even if the routine is pre-programmed or assisted, the useful part is visible: the robot has to strike, reset, and stay in rhythm without turning into random arm movement. Robot musicians may look funny today. They are quietly testing the same control problems needed for household robots, factory arms, and physical AI systems that work around humans. Would you call this entertainment, robotics research, or the early market for robot performers?

A humanoid robot playing a Yamaha drum kit is one of the cleanest ways to test rhythm control in public. Drumming exposes timing, wrist motion, impact force, arm recovery, and coordination between both hands. Even if the routine is pre-programmed or assisted, the useful part is visible: the robot has to strike, reset, and stay in rhythm without turning into random arm movement. Robot musicians may look funny today. They are quietly testing the same control problems needed for household robots, factory arms, and physical AI systems that work around humans. Would you call this entertainment, robotics research, or the early market for robot performers?

TechniaHQ | humanoid robots

208,048 次观看 • 3 个月前

One operator. Multiple live feeds. Total control. Our strike deck software lets you monitor multiple drones in real time, set waypoints on the fly, select targets, and execute precision strikes, all from a single interface. This is what scalable drone operations actually look like. #dronedominance #cuas #droneswarm #drones #dronewarfare

One operator. Multiple live feeds. Total control. Our strike deck software lets you monitor multiple drones in real time, set waypoints on the fly, select targets, and execute precision strikes, all from a single interface. This is what scalable drone operations actually look like. #dronedominance #cuas #droneswarm #drones #dronewarfare

Swarm Defense

18,441 次观看 • 5 个月前

Everyone is talking about OpenClaw mission controls. I rebuilt a $250K mission control using only one prompt. In this video you'll see: •The 3 levels of mission controls real builders are running •Why a mission control is critical to OpenClaw success •The exact prompt I used to generate the full dashboard in Lovable •My agent creating tasks in real time on the board instantly The full video is live on YouTube. Comment "Mission" and I'll DM you the link.

Everyone is talking about OpenClaw mission controls. I rebuilt a $250K mission control using only one prompt. In this video you'll see: •The 3 levels of mission controls real builders are running •Why a mission control is critical to OpenClaw success •The exact prompt I used to generate the full dashboard in Lovable •My agent creating tasks in real time on the board instantly The full video is live on YouTube. Comment "Mission" and I'll DM you the link.

Jacob Klug

28,014 次观看 • 6 个月前

Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,090 次观看 • 8 个月前

The Network Builder auctions are LIVE. For the first time, individuals and businesses can step forward to actively participate in building decentralized mobile infrastructure. This is what DePIN looks like when it moves from theory to execution. From interest to action. From maps to real-world networks. The next phase starts now. 👉 Learn how to bid 👇

The Network Builder auctions are LIVE. For the first time, individuals and businesses can step forward to actively participate in building decentralized mobile infrastructure. This is what DePIN looks like when it moves from theory to execution. From interest to action. From maps to real-world networks. The next phase starts now. 👉 Learn how to bid 👇

World Mobile

32,769 次观看 • 8 个月前

Most robotics AI models suffer from the "stop-and-think" problem. They take a static picture, pause to reason, execute an action, and repeat. In the real world, that latency causes spills, collisions, and failed tasks. Google DeepMind just launched Gemini Robotics ER 2: an embodied reasoning model that thinks and acts at the speed of the physical world. Here's why this is a step-change for physical AI engineering: Traditional robotics models rely on static snapshots. But knowing *when* a task is done, such as when to stop pouring coffee into a cup or when a trash bag is securely tied, requires continuous temporal awareness. Gemini Robotics ER 2 integrates directly with the bidirectional streaming Gemini Live API to reason about what comes next while simultaneously executing motor actions. What makes Gemini Robotics ER 2 different: 🎯 91.3% accuracy on live video moment-finding (0.96s mean absolute distance) at 4x the execution speed of frontier models 📈 Continuous progress tracking across 5 completion stages (57.4% accuracy) to self-correct mid-task without restarting 🛠️ Native agentic tool orchestration that commands lower-level VLA models, navigation APIs, and Google Search 🤝 Multi-robot collaboration allowing physically diverse machines (like Apptronik's Apollo 2 humanoid and Franka's FR3 Duo arm) to hand off tasks in shared spaces 🛡️ Built-in physical safety that autonomously halts robots when humans enter a workspace and resumes once clear

Most robotics AI models suffer from the "stop-and-think" problem. They take a static picture, pause to reason, execute an action, and repeat. In the real world, that latency causes spills, collisions, and failed tasks. Google DeepMind just launched Gemini Robotics ER 2: an embodied reasoning model that thinks and acts at the speed of the physical world. Here's why this is a step-change for physical AI engineering: Traditional robotics models rely on static snapshots. But knowing when a task is done, such as when to stop pouring coffee into a cup or when a trash bag is securely tied, requires continuous temporal awareness. Gemini Robotics ER 2 integrates directly with the bidirectional streaming Gemini Live API to reason about what comes next while simultaneously executing motor actions. What makes Gemini Robotics ER 2 different: 🎯 91.3% accuracy on live video moment-finding (0.96s mean absolute distance) at 4x the execution speed of frontier models 📈 Continuous progress tracking across 5 completion stages (57.4% accuracy) to self-correct mid-task without restarting 🛠️ Native agentic tool orchestration that commands lower-level VLA models, navigation APIs, and Google Search 🤝 Multi-robot collaboration allowing physically diverse machines (like Apptronik's Apollo 2 humanoid and Franka's FR3 Duo arm) to hand off tasks in shared spaces 🛡️ Built-in physical safety that autonomously halts robots when humans enter a workspace and resumes once clear

Karl Weinmeister

28,313 次观看 • 1 个月前

Crypto meets robotics. Superteam Georgia member's CrowdBrain on building on Solana and becoming the Colosseum grand champion: "When I started at Solana, I knew no one. We became hackathon winners in 2021, and we built one of the biggest gaming projects on Solana ever. You have to manifest it and put the work in, and everything's gonna happen." "Crypto allows you to create network effects and coordinate on a decentralized network. Robotics is missing the human layer. Robotics companies need humans to train robots, control them remotely, and collect real world data. Crowdbrain gives them trained operators and the infrastructure to do that at scale."

Crypto meets robotics. Superteam Georgia member's CrowdBrain on building on Solana and becoming the Colosseum grand champion: "When I started at Solana, I knew no one. We became hackathon winners in 2021, and we built one of the biggest gaming projects on Solana ever. You have to manifest it and put the work in, and everything's gonna happen." "Crypto allows you to create network effects and coordinate on a decentralized network. Robotics is missing the human layer. Robotics companies need humans to train robots, control them remotely, and collect real world data. Crowdbrain gives them trained operators and the infrastructure to do that at scale."

Solana

93,050 次观看 • 2 个月前

Imagine controlling a real robot from your home… no money, no experience needed. Sounds crazy, right? But it’s already possible. BitRobot 🦾 is building the world’s first open robotics lab powered by crypto incentives. Instead of one company doing everything, it connects people from all over the world to work together on real robotics and AI tasks. The network is made up of specialized subnets, each focused on different missions from collecting real-world data with robots to developing humanoid robots for everyday use. What makes it powerful? It uses crypto rewards to coordinate global resources like compute power, robot fleets, teleoperation time, and even human effort. This allows BitRobot to scale much faster than traditional labs. Now here’s the best part 👇 The easiest way to get involved right now is through TeleArms. You don’t need: – a robot – engineering skills – or any investment – Hardware All you need is a laptop and an internet connection. From your home, you can remotely control a real robotic arm inside BitRobot’s lab using your keyboard or mouse to pick up, move, and place objects. Every action you take helps generate real-world data that trains the next generation of AI to perform useful physical tasks. So you’re not just playing with a robot… You’re actually helping build the future of AI. I’ve been talking about BitRobot for a while, and now TeleArms is live! You can control a real robotic arm from home, but it’s in a private beta with limited access. I’m now an ambassador for BitRobot Network. I’m giving 4 exclusive access codes to my community so they can experience it too. A lot of people want to experience this, but since it’s limited, I decided to do a random giveaway. To participate in this giveaway : 1. Join the BitRobot Network Discord (Link in comments) 2. Come back to this post and comment below, explaining why you want to join TeleArms and how you plan to contribute. Note : Winner will be announced in the last 7 days. Once you do that, you’ll be in the running for one of the codes! Good luck, and I can’t wait to see your ideas!

Imagine controlling a real robot from your home… no money, no experience needed. Sounds crazy, right? But it’s already possible. BitRobot 🦾 is building the world’s first open robotics lab powered by crypto incentives. Instead of one company doing everything, it connects people from all over the world to work together on real robotics and AI tasks. The network is made up of specialized subnets, each focused on different missions from collecting real-world data with robots to developing humanoid robots for everyday use. What makes it powerful? It uses crypto rewards to coordinate global resources like compute power, robot fleets, teleoperation time, and even human effort. This allows BitRobot to scale much faster than traditional labs. Now here’s the best part 👇 The easiest way to get involved right now is through TeleArms. You don’t need: – a robot – engineering skills – or any investment – Hardware All you need is a laptop and an internet connection. From your home, you can remotely control a real robotic arm inside BitRobot’s lab using your keyboard or mouse to pick up, move, and place objects. Every action you take helps generate real-world data that trains the next generation of AI to perform useful physical tasks. So you’re not just playing with a robot… You’re actually helping build the future of AI. I’ve been talking about BitRobot for a while, and now TeleArms is live! You can control a real robotic arm from home, but it’s in a private beta with limited access. I’m now an ambassador for BitRobot Network. I’m giving 4 exclusive access codes to my community so they can experience it too. A lot of people want to experience this, but since it’s limited, I decided to do a random giveaway. To participate in this giveaway : 1. Join the BitRobot Network Discord (Link in comments) 2. Come back to this post and comment below, explaining why you want to join TeleArms and how you plan to contribute. Note : Winner will be announced in the last 7 days. Once you do that, you’ll be in the running for one of the codes! Good luck, and I can’t wait to see your ideas!

Apurba.Eth

36,318 次观看 • 5 个月前

Okay, full 5 second laugh break done. But wait… This tiny DIY robot made in China is already learning how to grab, move, and manipulate toys autonomously inside a real home environment. That is literally the first real step toward personal robotics and autonomous home manipulation. Toys are officially entering the robot era. At first you laugh, then two seconds later you realize this is exactly how robotics culture spreads: from labs running fully autonomous humanoids to factories to garages and now directly into kids’ bedrooms with DIY robots already doing miniature warehouse robotics. This is physical AI leaving the cloud and entering the living room. And honestly, it’s exciting watching this happen live on X right now.

Okay, full 5 second laugh break done. But wait… This tiny DIY robot made in China is already learning how to grab, move, and manipulate toys autonomously inside a real home environment. That is literally the first real step toward personal robotics and autonomous home manipulation. Toys are officially entering the robot era. At first you laugh, then two seconds later you realize this is exactly how robotics culture spreads: from labs running fully autonomous humanoids to factories to garages and now directly into kids’ bedrooms with DIY robots already doing miniature warehouse robotics. This is physical AI leaving the cloud and entering the living room. And honestly, it’s exciting watching this happen live on X right now.

TechniaHQ | humanoid robots

18,738 次观看 • 3 个月前

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 次观看 • 7 个月前

The number of fatal stabbings is at its highest level since records began. Campaigners say one of the only ways to reverse this is by having bleed control kits as accessible as defibrillators. Former gang member Carl Scott had his best friend die in his arms from a knife attack and he is now training people in how to use Bleed Control kits. ＠projectyouth23

The number of fatal stabbings is at its highest level since records began. Campaigners say one of the only ways to reverse this is by having bleed control kits as accessible as defibrillators. Former gang member Carl Scott had his best friend die in his arms from a knife attack and he is now training people in how to use Bleed Control kits. ＠projectyouth23

Good Morning Britain

234,466 次观看 • 3 年前