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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... show more

Forma Robotics

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40,233 просмотров • 6 месяцев назад •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! 🚀

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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

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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

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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.

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Qace Dynamics

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Megatron

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Humanoid

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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 🧠🔥

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TechniaHQ | humanoid robots

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Lukas Ziegler

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🚀 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

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SourceRobotics

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World Mobile

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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

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Triple inverted pendulum in transition control A classic control problem, done in real time. This setup moves smoothly between all eight equilibrium points of a triple inverted pendulum. The system reacts every 1 millisecond, which shows how fast modern control loops can be. • Real-time control at 1 ms sampling • Stable transitions between multiple balance points • Built with Simulink and LW-RCP02 hardware This is a beautiful example of how theory meets practice in advanced control engineering. Video: Credit: Embedded Control Lab, Inha University —- Weekly robotics and AI insights. Subscribe free:

Triple inverted pendulum in transition control A classic control problem, done in real time. This setup moves smoothly between all eight equilibrium points of a triple inverted pendulum. The system reacts every 1 millisecond, which shows how fast modern control loops can be. • Real-time control at 1 ms sampling • Stable transitions between multiple balance points • Built with Simulink and LW-RCP02 hardware This is a beautiful example of how theory meets practice in advanced control engineering. Video: Credit: Embedded Control Lab, Inha University —- Weekly robotics and AI insights. Subscribe free:

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ICYMI: Robotics history live on the community update yesterday. A pair of @Mentraglass was used to control robots by Unitree and PadBot through the Follow @Auki App-agnostic, device-agnostic spatial orchestration. We are hiring.

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