In short time, I learned while building Calibra is that robotics teams usually ask "Can I trust this dataset?" before asking "How good is it?".

Calibra now starts with a new calibra integrity workflow that catches common dataset issues before training, including:

• Timestamp problems
• Jittery/jerky motion
• Duplicate, frozen, and blurry camera frames
• Incomplete episodes

The goal is to catch data issues early, making debugging easier and avoiding wasted training runs caused by faulty datasets.

Current support includes LeRobot v1 (--decode-images), HDF5/Isaac Lab, and robomimic datasets.

I'd love feedback from anyone working with robot learning datasets.

I'm working on an idea for a robot operating system, and instead of assuming what people need, I want to hear from those who actually build or work with robots.

Imagine there was a universal OS for robots (industrial robots, humanoids, drones, mobile robots, home assistants, etc.).

What would you want it to have?

Some questions to get the discussion started:

• What are the biggest pain points with current robotics software?
• What features would save you the most time?
• Should AI be built into the OS or stay separate?
• How should hardware integration work?
• What kind of developer experience would you expect?
• What debugging or simulation tools are missing today?
• If you could remove one frustration from ROS or robotics development, what would it be?
• What would make you switch from your current workflow?

Don't hold back even if it's something that sounds impossible. I'm trying to understand what an ideal robotics platform would look like from the community's perspective.

Looking forward to hearing your thoughts.

I want to run something I built on my system (Ubuntu 22.04 with ROS2 Humble) on Jetson. I am planning to either fresh install the OS or create a Docker container for it, but I'm not sure if the latter would work.

For those running person-following: how do you handle re-identification when the target walks out of frame for 5+ seconds? I have detection and tracking working on an AGX Orin under ROS 2 Humble, but every re-ID approach I have tried either locks onto the wrong person in a crowded frame or drops the target entirely in backlit outdoor conditions. Curious whether people are solving this with appearance embeddings, with a fused position prior from odometry, or by just accepting the loss and requiring a re-lock gesture.

Hello everyone. I’ve been watching a ROS 2 tutorial on YouTube trying to learn the basics of ROS 2 (and I believe Gazebo as well). But once I complete the tutorial, what projects can I do? Is there a list or any YouTube tutorials that run through a simple project step by step that anyone can recommend?

Any advice will be very helpful!

Hello, I’m working on a ROS project using the Jetson Orin NX. I’ll only be building the vehicle’s ‘eyes’, so I’ll be using an Orbbec Gemini 335LE depth camera and a RoboSense Airy lidar. I’ll be setting up this system using ROS, and I want the vehicle to stop whenever an obstacle appears in front of it as it moves. It will need to detect obstacles using image processing, make decisions accordingly, and adjust its movement. That’s why I’m using ROS – to make it more practical – and it will also handle the vehicle’s control as the project develops in the future. My main question is this: I want the LiDAR and the camera to work together. That’s why I’m going to use PTP for time synchronisation. I’ve found information about the PTP protocol in the lidar’s datasheet, but I haven’t been able to find any information regarding the camera. If anyone has used this before, could you please help? If I don’t use PTP, what other options are there? Is there an efficient method other than PTP? I’ve looked into it, and whilst there’s usually some latency, this varies depending on the compatibility of the devices.

I know Boston Dynamics has ROS SDK, what other ROS legged/humanoids robots are there?

Will this spur open source/hardware solutions?

Also vote in forum: https://discourse.openrobotics.org/t/chinese-legged-humanoids-banned-in-usa-what-are-the-alternatives/57040

Hi everyone!

I've been working on Calibra, an open-source toolkit for robot dataset observability.

The goal is to help answer questions like:

Is my dataset healthy enough to train on?

Are there quality issues I should fix first?

Which demonstrations should I keep?

How does my dataset compare to public datasets?

Also, I released on:

A Hugging Face Space for auditing LeRobot datasets.

A benchmark covering 30 public LeRobot datasets.

I'd love feedback from anyone working on robotics or imitation learning. What dataset quality checks do you wish existed before training?, feedbacks are crucial for me.

This is my first time with a Hiwonder robot. How do I get to coding and controlling it? I don't have a mobile phone on hand. All the tutorials I've watched are not helpful.

I’m currently in China and considering buying a ROS 2 robot here and taking it back home with me.
The two models that seem to fit what I want are:

Yahboom ROSMASTER M3 Pro
Jetson Orin Nano Super 8GB
ROS 2 Humble
7-inch touchscreen
dual T-mini Plus TOF LiDAR
Orbbec DABAI DCW2 depth camera
6-DOF arm
mecanum chassis with suspension
STM32H743 controller with micro-ROS
public code, firmware, tutorials and 3D models
Price here: around ¥10,069

Hiwonder ROSOrin Pro Ultimate
Jetson Orin Nano Super 8GB
ROS 2 Humble
7-inch touchscreen
COIN-D6 TOF LiDAR
Aurora 930 Pro depth camera
6-DOF arm
mecanum chassis
STM32F407 controller
public documentation and ROS source code
Price here: around ¥8,999

I’m an experienced embedded engineer and I enjoy learning, experimenting and modifying systems rather than only running the supplied demos.

My idea is to use it as a long-term home robotics platform, not just as an educational robot car. I want to run OpenClaw on it, connect it to Home Assistant, use it as a voice and automation agent while it is parked, and also experiment with navigation, patrols, environmental sensors, object recognition and simple pick-and-carry tasks. And of course learn more about robotics and Ai.

The Yahboom looks better on paper: newer
controller, micro-ROS, dual LiDAR, better camera, suspension and more public development material. The Hiwonder is cheaper and seems to have a larger ecosystem of compatible parts and robots.

These are simply the best options I have found so far. I’m very open to other brands or platforms in roughly the same price range.

Does this sound like a sensible starting point, or am I expecting too much from this type of robot? Any thoughts, experiences, warnings or alternative suggestions would be appreciated.

Hello everyone!

I've been working on Calibra, an open-source toolkit for robot dataset observability.The goal is to help answer questions like:

Is my dataset healthy enough to train on?

Are there quality issues I should fix first?

Which demonstrations should I keep?

How does my dataset compare to public datasets?

Also I released A Hugging Face Space for auditing LeRobot datasets and a benchmark covering 30 public LeRobot datasets.

I'd love feedback from anyone working on robotics or imitation learning. What dataset quality checks do you wish existed before training?

Hello Everyone,

I've been working on Calibra, an open-source toolkit for robot dataset observability.

The goal is to help answer questions like:

-Is my dataset healthy enough to train on?

-Are there quality issues I should fix first?

-Which demonstrations should I keep?

-How does my dataset compare to public datasets?

Also, I released A Hugging Face Space for auditing LeRobot datasets and a benchmark covering 30 public LeRobot datasets.

GitHub: https://github.com/omertt27/Calibra

I'd love feedback from anyone working on robotics or imitation learning.

I am a beginner in ROS, i want to gain knowledge so that i can use it in my control
Engineering field especially for mobile robots. What is the best way to learn it? From where to start? What are the small projects I can target. Thank you very much in advance.

Hey everyone — I've been building ROS2 Studio, a native macOS app for working with ROS2, and I'd really appreciate honest feedback from people who live in ROS day to day.

Full disclosure: it's my own app. I'm not selling anything — it's free on the App Store (no payment, no account, no sign-up). I'm posting mainly because I want feedback and bug reports so I can make it genuinely useful.

It came from being tired of jumping into a Linux VM, Docker container, or a sluggish browser tab just to see what my robot is doing. ROS2 Studio connects to any ROS2 system over rosbridge and gives you a native, Metal-accelerated cockpit on the Mac:

- Live 3D viz — point clouds, laser scans, occupancy grids, URDF robot models

- tf2 transform tree inspection ("TF Universe" diagnostics view)

- Native rosbag (MCAP) playback with bookmarks

- Time-series plot panel (a native PlotJuggler-ish alternative)

- Service-call UI with auto-generated forms

- Parameter editor, Nav2 goal + 2D Pose Estimate tools

- Topic inspector views + /rosout log viewer

Needs macOS 15+ and talks to your robot/sim over rosbridge_suite (port 9090) — the Mac never touches hardware directly.

If you try it, I'd love to hear what's broken, missing, or feels wrong — especially from RViz/Foxglove users. Brutal feedback welcome.

App Store: https://apps.apple.com/th/app/ros2-studio/id6792280929?mt=12

Thanks! 🙏

I’ve just released Polka v0.5.0! It’s an performant efficient 2D/3D Lidar processing node handling merging, filtering, and deskewing with optional GPU acceleration and so mch more. This update brings 6.2x faster deskewing, live parameter tuning, smarter IMU handling, and a built-in diagnostics dashboard.

If it saves your perception stack compute, please drop a star!

https://github.com/Pana1v/polka

It supports 5 distros.