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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...

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Forma Robotics

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40,274 görüntüleme • 10 ay önce •via X (Twitter)

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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! 🚀
0:35

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

17,471 görüntüleme • 5 ay önce

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.
0:39

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

65,989 görüntüleme • 1 yıl önce

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 →
0:56

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

21,042 görüntüleme • 9 ay önce

Today we’re using a arm to prototype our first RaaS workflow — soon, the arm doing this will be ours. Using a Franka arm, we prototyped the pick-and-place of Forma logo, a pipeline that will run on the Forma robotic arm: • trajectory planning • precision gripping • closed-loop positioning • object handling It’s our first RaaS workflow running in the real world. We’re building RaaS like software: test → ship → iterate. Next: deploy the same pipeline on the Forma arm and bring automation to the cloud.
0:26

Today we’re using a arm to prototype our first RaaS workflow — soon, the arm doing this will be ours. Using a Franka arm, we prototyped the pick-and-place of Forma logo, a pipeline that will run on the Forma robotic arm: • trajectory planning • precision gripping • closed-loop positioning • object handling It’s our first RaaS workflow running in the real world. We’re building RaaS like software: test → ship → iterate. Next: deploy the same pipeline on the Forma arm and bring automation to the cloud.

Forma Robotics

31,082 görüntüleme • 1 yıl 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 • 11 ay önce

Robotics is moving beyond teaching robots how to perform a fixed set of actions. nature will’s new foundation model is built around a more general problem: can a robot understand an instruction, adapt to the situation in front of it, and physically execute the task? What makes this interesting is the combination of dexterous control, zero-shot generalization and long-horizon execution in one model.
3:12

Robotics is moving beyond teaching robots how to perform a fixed set of actions. nature will’s new foundation model is built around a more general problem: can a robot understand an instruction, adapt to the situation in front of it, and physically execute the task? What makes this interesting is the combination of dexterous control, zero-shot generalization and long-horizon execution in one model.

Fathela Esq

85,798 görüntüleme • 24 gün önce

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.
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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

43,303 görüntüleme • 5 ay önce

Today, we’re introducing KinetIQ, our own AI framework for end-to-end orchestration of humanoid robot fleets. One system, multiple robot embodiments. Industrial, service and home environments coordinated in real time. The framework consists of 4 cognitive layers: from high-level task allocation and workflow optimisation down to VLA-based task execution and RL-trained whole-body control. Watch how KinetIQ runs both our wheeled and bipedal robots. Read more on our blog:
3:47

Today, we’re introducing KinetIQ, our own AI framework for end-to-end orchestration of humanoid robot fleets. One system, multiple robot embodiments. Industrial, service and home environments coordinated in real time. The framework consists of 4 cognitive layers: from high-level task allocation and workflow optimisation down to VLA-based task execution and RL-trained whole-body control. Watch how KinetIQ runs both our wheeled and bipedal robots. Read more on our blog:

Humanoid

23,975 görüntüleme • 9 ay önce

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 🧠🔥
1:32

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

62,932 görüntüleme • 1 yıl önce

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?
0:20

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?

Techniahqrobot | humanoid robots

210,181 görüntüleme • 5 ay önce

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
0:29

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 görüntüleme • 7 ay önce

🚀 Launching our open-source teleoperation device for full-body Vega robot control! 🔓 Open-source 💰 Low-cost 🎒 Portable 🦾 Whole-body control Control both real and simulated Vega robots! 🤖✨ 📍 Live Demo at #CoRL - See it in action with real and simulated Vega robots in Isaac Lab. Stop by booth #6 if you're attending! #PhysicalAI #Robotics #Teleoperation
0:34

🚀 Launching our open-source teleoperation device for full-body Vega robot control! 🔓 Open-source 💰 Low-cost 🎒 Portable 🦾 Whole-body control Control both real and simulated Vega robots! 🤖✨ 📍 Live Demo at #CoRL - See it in action with real and simulated Vega robots in Isaac Lab. Stop by booth #6 if you're attending! #PhysicalAI #Robotics #Teleoperation

Dexmate

14,529 görüntüleme • 1 yıl önce

One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of Waddle Labs and Robocurve PBC, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. 00:00 — What Are Robot-Use Agents? 02:11 — From Vision-Language-Action Models to General LLMs 04:07 — The Bitter Lesson for Robotics 07:04 — How Coding Agents Learned to Control Robots 10:41 — How Robots Learn From Experience 14:22 — The Harness as a Form of Robot Intelligence 16:14 — Watching Astra Control a Robot 20:55 — Why General Models May Win in Robotics 26:05 — How Close Are General-Purpose Robots?
29:49

One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of Waddle Labs and Robocurve PBC, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. 00:00 — What Are Robot-Use Agents? 02:11 — From Vision-Language-Action Models to General LLMs 04:07 — The Bitter Lesson for Robotics 07:04 — How Coding Agents Learned to Control Robots 10:41 — How Robots Learn From Experience 14:22 — The Harness as a Form of Robot Intelligence 16:14 — Watching Astra Control a Robot 20:55 — Why General Models May Win in Robotics 26:05 — How Close Are General-Purpose Robots?

Y Combinator

95,405 görüntüleme • 1 ay önce

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.
0:43

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

28,063 görüntüleme • 8 ay önce

Today, Dream Labs is out of stealth. In February 2026, while we were at NVIDIA, Scott Reed youliang Yunhao Ge and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act. Today, we’re releasing Dream-1 through a public API that anyone in the world can use. When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch. That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments. Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives. Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users. Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen. Generalization wins in the end, and the end is closer than it looks. Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇
0:27

Today, Dream Labs is out of stealth. In February 2026, while we were at NVIDIA, Scott Reed youliang Yunhao Ge and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act. Today, we’re releasing Dream-1 through a public API that anyone in the world can use. When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch. That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments. Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives. Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users. Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen. Generalization wins in the end, and the end is closer than it looks. Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇

Joel Jang

35,514 görüntüleme • 18 gün önce

What does it take for a robot to handle one of the most delicate forms of traditional craftsmanship? Suzhou embroidery is one of China's four famous embroidery traditions. It is over a thousand years old and is listed as national-level intangible cultural heritage. The work happens at the scale of a single silk fiber: a thread is split into finer strands, twisted, picked, and passed through the eye of a needle. One small slip shows in the final piece. In a collaboration with the Suzhou Silk Museum and embroidery inheritor Fu Xianghong, AGILINK's OmniHand 3 Ultra learns these techniques. Shot entirely live action, without CGI. I sat down with robotics engineer Kevin Wood | Robotics & AI to talk about what most coverage skips: why fine manipulation needs a human-scale hand, what fingertip sensing changes about force control and where the training data for hands like this actually comes from. When robotics meets centuries-old craftsmanship, it stops being a tech demo. It shows how close robot hands are getting to real, skilled work.
8:56

What does it take for a robot to handle one of the most delicate forms of traditional craftsmanship? Suzhou embroidery is one of China's four famous embroidery traditions. It is over a thousand years old and is listed as national-level intangible cultural heritage. The work happens at the scale of a single silk fiber: a thread is split into finer strands, twisted, picked, and passed through the eye of a needle. One small slip shows in the final piece. In a collaboration with the Suzhou Silk Museum and embroidery inheritor Fu Xianghong, AGILINK's OmniHand 3 Ultra learns these techniques. Shot entirely live action, without CGI. I sat down with robotics engineer Kevin Wood | Robotics & AI to talk about what most coverage skips: why fine manipulation needs a human-scale hand, what fingertip sensing changes about force control and where the training data for hands like this actually comes from. When robotics meets centuries-old craftsmanship, it stops being a tech demo. It shows how close robot hands are getting to real, skilled work.

Shruti

116,817 görüntüleme • 7 gün önce