Alex

A full MIT course on robot mechanics and control. If you're building or working with robotic systems, this one deserves a permanent bookmark.📌 Russ Tedrake's Underactuated Robotics at MIT covers the math and intuition behind how robots actually move — not just the surface level. No paywalls. No prerequisites gatekeeping. What it focuses on: - Nonlinear dynamics and stability for robotic systems - Trajectory optimization and motion planning - Reinforcement learning applied to locomotion and manipulation - Lyapunov methods and limit cycles for real control design - Worked examples with code across the full course Full textbook, lecture videos, and problem sets — all free. 📍 underactuated.mit.edu

キャリブレーション(機械的な)が必要ですね。

CUDA programming course for GPU optimization github.com/Infatoshi/cuda…

Really excited to release mjviser, a web-based MuJoCo viewer, powered by Viser. It has almost all the features of the native MuJoCo viewer, but runs in your browser. Load and simulate any MuJoCo model with a single uv command 👇 uvx mjviser

Last week, we announced our three new hands. Today, we're releasing their digital twins ↓↓↓ > new orcahand mjcf/urdf files available on github.com/orcahand/orcah… > custom learning environment @ github.com/orcahand/orca_…

Web-based robot viewer and simulator github.com/fan-ziqi/robot…

Robot implementations in MuJoCo Warp for locomotion and motion imitation github.com/pal-robotics/p…

If You Love Mathematics and Physics, You'll Love Control Systems Episode 1 Control Systems are the craft of keeping something doing what you want, even when the environment is pushing back. You simply measure what's happening, compare it to your goal and apply correction over and over, many times per second. We need Control Systems because the real world is noisy and unforgiving. Loads change, wind happens, sensors lie, actuators saturate, and tiny errors snowball into failure unless you actively stabilize. In this animation, a cart must keep an upside down stick from falling while we shove it, add gusts, change the weight mid-run, and force it to track new positions. The Controller keeps nudging and braking so it stays upright instead of tipping over. Subscribers can get Python Script on Request.

Resources for GPU engineering and AI acceleration github.com/goabiaryan/awe…

Run your robot in simulation! 🖲️ 📌 If you’re self-learning robotics, this is genuinely one to save for later. This is next chapter of @NVIDIARobotics course "Getting Started with Isaac Sim" covering everything from building your first robot to hardware-in-the-loop deployment. Today you will learn how to import, configure and... FINALLY simulate your cute robot. Quick look what's inside: → Analyze URDF Structures: Examine URDF file structure and components to identify key elements like joints and links. This forms the basis for importing and configuring robots in Isaac Sim. → Apply the URDF Importer: Use Isaac Sim's URDF Importer to successfully import and simulate robot models. Set appropriate import options to ensure accurate representation and functionality. → Design Control Systems: Create control systems using differential controllers and keyboard control interfaces, enabling dynamic movement and interaction in the simulated environment. → Evaluate Physics Behavior: Assess simulated robot physics to identify and resolve issues like excessive velocity or incorrect joint configurations, ensuring realistic interactions. → Create Simulated Environments: Develop complete environments in Isaac Sim with robot models, appropriate physics and control settings, obstacles, and sensor configurations. The module builds on previous foundations, preparing you for more advanced simulations and applications in the next module. This is NVIDIA's structured approach to lowering the Isaac Sim learning curve. Most robotics teams have existing URDF files from their robot designs. Being able to import those directly into simulation without manual rebuilding accelerates iteration significantly. See you next week! Send this to your friend that wants to learn robotics! 💚 Here's the course (it's free): docs.nvidia.com/learning/physi… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Your app has two halves. Frontend: everything that runs on your user's device. Their browser. Their screen. Their machine. Backend: everything that runs on your server. Your logic. Your database. Your secrets. The line between them is the internet. Every feature you build crosses it.

🚨 BREAKING: Someone compiled every sim-to-real RL workflow for Unitree robots in one GitHub repo. You can deploy: ∙Trained MuJoCo policy → real G1 humanoid ∙Trained MuJoCo policy → real H1 humanoid ∙Trained MuJoCo policy → real Go2 quadruped In one afternoon. → github.com/unitreerobotic…

Learn how to build robotics simulators entirely in the browser MuJoCo (WebAssembly) + Three.js + Gemini ER

NASA's An Introduction to Tensors for Students of Physics and Engineering by Joseph C. Kolecki PDF: grc.nasa.gov/WWW/k-12/Numbe…

Hardware kit for robot learning github.com/robot-learning…

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: @embedder_dev

A full MIT course on visual autonomous navigation. If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️ MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms. What it focuses on: • 2D and 3D vision for navigation • Visual and visual-inertial odometry for state estimation • Place recognition and SLAM for localization and mapping • Trajectory optimization for motion planning • Learning-based perception in geometric settings All material is available publicly, including slides and notes. 📍vnav.mit.edu If you know other solid resources on vision-based autonomy, feel free to share them. —- Weekly robotics and AI insights. Subscribe free: scalingdeep.tech

Another RL team replicated our Physical Atari work and compared my baseline agent against several standard algorithms. robo-atari.com/report

Reinforcement learning 🧠 on robots 🤖 can’t stay in simulation forever. My new post explores why direct, on-hardware learning matters and how we also need smarter mechanical design to enable it. kris.pengy.ca/designforlearn…

github.com/SIGRobotics-UI… Open source low cost mobile manipulator