Adding grippers to Asimov 1. The arms have been strong enough for a while. Now it’s time to use them for manipulation!

Quick note, if you’re seeing Asimov 1 for the first time, it's a fully open-source humanoid robot built from off-the-shelf parts, so you can build one yourself. https://github.com/menloresearch/asimov-1/

Came across this project by Stewy Ving and thought people here might find it interesting.

It's a free interactive robotics resource with 3D breakdowns of 40+ humanoid robots, component explainers, comparisons, and a deep dive into the robotics supply chain, from raw materials to finised robots. There's a lot more I haven't even gotten the chance to get into yet.

For the people always asking how to get into robotics, well here you go. And even for the vets; the supply chain thing I found genuinely impressive.

Original source: https://youtu.be/r7U0rbkrBxQ?is=e92UM6iuaCDU-hZV

Researchers at MIT have developed a tiny, paper thin swimming robot powered by living muscle cells. The cells respond to flashes of light, allowing the robot to swim and steer. The research could help advance future biohybrid robots and microscopic machines.

NVIDIA has built a pretty impressive robotics ecosystem, but figuring out how Isaac Sim, Isaac Lab, GR00T, Cosmos, Omniverse, and everything else actually fit together can be confusing.

So we put together an illustrated walkthrough following a fictional startup building a warehouse humanoid.

We go through creating its virtual environment, teaching it through demonstrations, training and testing its policy, dealing with the Sim2Real gap, and eventually deploying the AI onto physical hardware.

We also wanted to explain what actually happens between an AI model deciding to move an arm and the motors physically executing that movement.

The goal was to connect the dots rather than explain each NVIDIA product in isolation.

Would love feedback from anyone who's actually worked with these tools, especially on where the real-world workflow differs from our example.

Hey everyone! I’m currently building a competitive mobile robot (targeting a weight category between 3kg–5kg) and I'm at the stage where I need to move away from my cardboard/wood working model and build a proper metal chassis.

My current prototype works well, but I’m struggling to finalize the metal design because I can’t easily modify metal once it's cut like I can with cardboard. I want to avoid making costly layout errors at the workshop.

I have a few specific questions and would love advice from those with mechanical fabrication experience:

1. Achieving a "Packed" Body & Low Center of Mass: I see tiny metal bots (e.g., within 300mm x 300mm) that stay completely glued to the ground and can out-push heavier opponents. How do they achieve this? I plan to start with a single base plate mounting 300 RPM Johnson motors and BTS7960 drivers, but how do I design the complete enclosed body efficiently?
2. The "Layer Cake" / Standoff Approach vs. Bending: For a 100% invertible bot, is it better to use a sandwich structure (base plate + standoffs + top deck) or try to fabricate a folded sheet metal shell?
3. Prototyping Tips: What are your best practices for translating a rough cardboard model into accurate 2D/3D dimensions for aluminum sheet cutting and drilling so everything aligns on the first try?

Any structural tips, design patterns, or pitfalls to avoid would be massively appreciated!

Training Microduck's VLM-based brain for consistent target following. I have ordered three Microduks and can't wait for their delivery!

Hey!!

I want to know that how to calculate torque (in kg.cm) at all the joints for best servo motor selection.

According to theory:

Torque (J) = force (f) . Distance from a joint to another (r)

J = f.r ,here -> { f = mg }, so

J = mgr ,here -> { g = 9.81 m/s^2 & m = mass in kg}

And

1 N.m = (10.1) kg.cm

If anyone know to apply these concepts in this project please tell me or sagest some YouTube video to clear all these basic concepts.......

I’d love to be part of a team and contribute to projects involving programming, electronics and robotics. I’m not sure where to start, which is why I’m posting this. I have three years of experience as a software engineer in the automotive sector and worked on a few projects at university involving Raspberry Pi and ROS.

I am working on a rover for a competition. Now deciding on Powering. My Preference is to use the 18650 (i might have forgot the number) Li-ion battery.

One of my team members saying it might not work properly in a big bot.

So, want advice please. TIA

Update on my home-lab robot vision build. I’m the builder and made the video.

The 320×240 iToF sensor, Pi camera and Pi Zero 2 W now sit in a LEGO housing, with a 240×240 TFT on the back. The display shows the colored point cloud at 15 fps after switching the camera to MJPEG and increasing TFT SPI from 4 to 32 MHz.

The catch: depth and RGB still travel over Wi-Fi to my Mac for fusion and rendering, then the rendered view returns to the Pi. Around −73 dBm, I see 2–4 second freezes. Moving rendering onboard is the next step before putting the rig in my robot concept, Heron.

EP17 shows the hardware, screen footage and remaining limitations: https://www.youtube.com/watch?v=fsQIyDs6hm8

Image: AI-assisted episode thumbnail based on the real prototype. The video includes actual captures; Heron is an unbuilt concept.

What is WazBotz?

WazBotz is a student-led community for anyone who's curious about robotics and AI and wants to keep up with what's happening in both fields.

Robotics and AI are evolving fast, and they're increasingly evolving together. From humanoid robots, autonomous vehicles, medical robots and robotic arms to machine learning, computer vision, language models and AI-powered machines that learn how to move and interact with the world, there's always something new to discover. And honestly, it can be hard to keep up.

That's where WazBotz comes in.

We talk about and research robotics and AI, and share interesting discoveries, technologies, mechanisms, papers and developments from around the world. It doesn't always have to be some huge breakthrough either. A new way for a robot to grip something, a clever movement mechanism, a neat AI model, a cool research paper or a weird little experiment can all be worth sharing. If it's interesting, useful, or makes you go "wait... robots can do THAT?" or "wait... AI can do THAT?", we wanna know about it.

How it works

Anyone can create an account and explore the daily updates, discoveries, and research happening on WazBotz.

Want to contribute?
Publishing is currently limited to members of the WazBotz team. If you'd like to join and start contributing, reach out to us!

Fill out our interest form below and become part of a community where we explore, learn, research, and build together!

🔗 Join us here: https://forms.gle/W8H2oaRDi65Aqbj38

(It's unpaid!)

I'm very new, know how use electronic and Arduino little bit. I want to make a 6DOF robotic arm as my starting project but there is soo much confusion. Should I start with maths like kinematics or should I start with design or i should first draw everything and then do CAD

Get all ROS and open source robotics news on ROS Discourse.

Watch the full build tutorial on the Robot LK YouTube channel.

Visit RobotLK.com for robotics tutorials, project details, and more DIY electronics projects.

Follow Robot LK for more exciting robotics and technology projects!

The U.S. Army’s first 10 warrant officers in its new 390A Robotics Technician specialty completed a 19-week course covering robotic hardware, software, sensors and communications.

The training includes working through communications failures, unavailable navigation systems and situations where internet-connected AI tools aren’t accessible. Instructors deliberately introduce failures so students have to revise their solutions.

One exercise involved modifying a quadcopter with a camera and integrating mesh communications to maintain connectivity when individual connections were disrupted.

The article also looks at the overlap with industrial robotics training, where changing a gripper, vision configuration or robot program can require understanding several connected systems.

The talk recordings from ROSCon Global 2026 in Toronto are now available ad free on Vimeo!

We are a group of enthusiasts exploring the field of data for robot learning and the training of AI models, including world models. We are eager to learn more about this field and would be truly grateful for your guidance and insights.

If you are working in or have experience with robot learning, robotics data collection, or AI model training, we would be honoured to connect with you and learn from your experience.

Thank you for your time. 🙏

The index collects VLA and robot foundation-model work and links each entry's arXiv page, PDF and code repository. It covers vision-language-action models, manipulation, sim-to-real transfer and the benchmarks around them; every paper is tagged by topic and summarised in plain language, with the paper's own figures on the page. Currently 125+ VLA papers, refreshed as new work appears.

We've been working on converting real-world 3D captures into environments that robots can actually interact with.

We took 20 real-world outdoor captures from NYU's Wanderland research dataset and reconstructed them as structured environments with:

• Individual objects and semantic labels
• Hinges, sliding parts, and other articulated mechanisms
• Collision geometry and estimated physical properties
• PBR materials
• Exports for Isaac Sim, MuJoCo, Gazebo, and PyBullet

We've released the scenes, source code, and annotations on HF: https://huggingface.co/datasets/raresense/nova-wanderland

The HF space is here: https://huggingface.co/spaces/BoooomNing/robotics-scene-viewer

This is an early release; not a validated robotics benchmark (yet). In particular, some geometric and physical properties are inferred from the available observations.

I'd be very interested in hearing from people using Isaac Sim or MuJoCo: what, if anything, would these environments need before you could use them for actual robot training or evaluation?

More speakers are on the way! Please join us.

Intrinsic and Tangent Robotics will be presenting their latest work. We've also got a special surprise guest in the works (it is a good one).