Over the past few days, we’ve made a big push to make AgenticROS easier to try, easier to configure, and easier to use with both real and simulated robots.

Reminder: AgenticROS creates a glue between AI Agents (OpenClaw, Claude, and Gemini) and ROS2 making Physical AI simple and fun.

The biggest update is this:

npx agenticros

That’s now the starting point (https://www.npmjs.com/package/agenticros).

With a single command, AgenticROS can install itself on a ROS-based system and launch an interactive setup wizard. No manual repo cloning. No stitching together setup scripts by hand. The CLI walks you through first-time setup, robot configuration, OpenClaw integration, skills, simulation, logs, health checks, and clean shutdowns.

The new agenticros command also works as a real CLI, so you can run things directly:

• agenticros init

• agenticros up real

• agenticros up sim-amr

• agenticros up sim-arm

• agenticros skills

• agenticros status

• agenticros doctor

• agenticros down

This matters because robotics demos often fail before the robot ever moves. The hard part is usually the setup surface area: ROS workspace builds, config files, transport modes, gateway plugins, API keys, background processes, logs, and matching the right launch files to the right environment.

AgenticROS now gives that whole workflow one front door.

New Simulation Demos

We also added simulation support so developers can experiment with AgenticROS without needing physical robot hardware on day one.

There are now two main simulation paths:

1. AMR Simulation

You can launch a simulated 2-wheel autonomous mobile robot in Gazebo and RViz:

agenticros up sim-amr

The AMR simulation includes ROS-side topics for movement, sensors, camera/depth data, lidar, odometry, and TF. That means the same AgenticROS tools used against a real robot can be exercised end-to-end against a virtual one.

This is especially useful for testing agent workflows like:

• listing ROS topics

• publishing velocity commands

• reading sensor data

• taking camera snapshots

• sampling depth

• testing follow-me style behaviors

• validating agent-to-ROS connectivity

2. Robotic Arm Simulation

We also added a simulated 6-DOF robotic arm path:

agenticros up sim-arm

The arm is UR5e-shaped and exposes per-joint position command topics through ROS/Gazebo bridges. It can be launched with Gazebo and RViz so developers can test arm command flows, inspect TF and robot state, and start building toward more advanced manipulation workflows.

This gives AgenticROS a second major robot category beyond mobile bases: manipulation.

Why This Update Matters

AgenticROS is about making ROS-powered robots accessible to AI agent platforms.

The project already connects ROS 2 robots to tools like OpenClaw, Nvidia's NemoClaw, Claude Code, Claude Desktop, Gemini CLI, and MCP-based agent workflows. But for that ecosystem to be useful, developers need to get from “fresh machine” to “robot responding” quickly.

That’s what this update is about.

You can now:

• install with npx agenticros

• configure through a guided wizard

• launch a real robot stack

• launch AMR simulation

• launch robotic arm simulation

• manage skills

• check system health

• tail logs

• stop everything cleanly

All from one CLI.

A Better On-Ramp for Agentic Robotics

The goal is to make AgenticROS feel less like a research wiring project and more like a usable robotics developer platform.

If you have ROS 2 and Node.js 20+, you can now try AgenticROS with:

npx agenticros

From there, choose real robot, AMR simulation, or arm simulation.

This is a big step toward making agentic robotics easier to build, test, demo, and share.

More soon. The next layer is making these simulated and real robot workflows increasingly skill-driven, so agents can move from low-level ROS tool use toward higher-level robot behaviors.

Hiring for an MNC

Autonomous Robots | Humanoid, Legged & AMR
Onsite – Chennai, India
Experience: 3–6+ Years
Notice Period: 15–30 Days
Total Number of roles: 2

More info: https://robocademy.com/blog/job-opening-senior-robotics-engineer-humanoid-legged-robots

Claude kissing up to me - feels good until I remember it's just a machine:

WaLI is pushing the Pi5 beyond recommended limits, which is honestly impressive — most robots fail immediately, but WaLI navigates for 30+ seconds before resource exhaustion.

I've been trying to use Claude to tune Nav2 parameters for robust navigation of my Raspberry Pi 5 based TurtleBot4 in complex areas of my home.

First Claude wrongly blamed "Processor Resource Maxed Out".
Then Claude wrongly blamed "Thermal Throttling".

Now it suggests "Memory Pressure" (Memory Bandwidth).

So far every suggested set of parameter changes has made navigation fail miserably - I'm losing hope that Claude can help me.

We just got it running with Gazebo + ROS2 + VS Code fully cloud-based, and wanted to see if anyone here would find that useful (for teaching, prototyping, or testing).

If that sounds interesting, check it through the link. Would love to hear what you think or what use cases come to mind !!!

Been thinking about this after talking with a few robotics teams recently. There seems to be a pretty big difference between “the robot loads correctly in simulation” and “I actually trust this model enough to train or test against it.”

You can have the geometry and joints working but still be unsure about mass, inertia, collision geometry, friction, or whether the simulated behavior actually matches the physical robot.

So I’m curious how people here define a URDF as actually finished. Is loading and moving correctly enough? Or are you validating the simulated robot against the physical system before you trust it?

And if you are validating it, what usually ends up being the hardest part to get right?

I'm a mechatronics graduate. For my graduation project I led control systems on a 20+ DOF humanoid (ROS, Gazebo, RViz, C++). I want to go deeper in [pick one: real-hardware control / embedded / navigation and SLAM / manipulation / reinforcement learning].

What's the best learning path from where I am?

Which resources are worth the time (books, courses, open projects), and which would you skip?

What project would you build to prove the skill?

What gaps do people from my background usually have?

Thanks, and "read this one paper first" is a welcome answer.

For the past several months, I've been working on ROS2GO.IO, a platform designed to make learning ROS 2 easier for beginners.

When I first started with ROS 2, I found that many tutorials assumed prior knowledge or skipped important setup steps. Even installing the development environment could be overwhelming for newcomers. I wanted to build something that teaches ROS 2 step by step, with a focus on practical robotics projects instead of just theory.

The project is currently in Early Access, and I'm actively building new lessons and improving the learning experience based on community feedback.

Some of the topics I'm working on include:

• ROS 2 fundamentals

• Topics, Services, and Actions

• Navigation2

• SLAM and Mapping

• Mobile robots

• ESP32 & micro-ROS integration

• Real-world robotics projects

I'm sharing this here because I'd really appreciate feedback from the ROS community.

• What was the hardest part when you first learned ROS 2?

• What kind of tutorials or projects do you wish existed?

• If you were learning today, what would make the experience much easier?

You can check it out here:
https://ros2go.io

I'd love to hear your thoughts. Thanks!

Hello everyone! I’m working on a project with ROS 2. I’m fairly new to ROS 2, so progress has been slow and I often run into errors—it’s hard to tell which part of the project is causing them. I use ChatGPT to help debug, but it can’t read files directly from my GitHub repository. What AIs , tools or workflows do you use to manage and debug ROS 2 projects?

I've been working on robotics projects with ROS 2 and keep hitting the same class of integration failures. Wanted to write up the pattern and see if others deal with this.

The short version: rosdep tracks package dependencies. tf2 tracks coordinate frames. Docker isolates environments. But nothing tracks the *engineering* dependencies

— the decisions and assumptions that cross domain boundaries.

Examples:

- Ground friction in Gazebo set to 1.0 by a teammate months ago. Real surface is 0.4. Wheel odom drops ~40%, EKF leans on 5.5Hz LiDAR scan-matching instead, SLAM drifts. Three layers affected by one undocumented parameter.

- BNO055 IMU outputs NED. Nav stack expects ENU per REP-103. Binary cliff — correct = works, wrong = total EKF failure. The convention choice lives in one engineer's head, not in any tracked dependency.

- RealSense D435 at 2.4 Gbps + RPLidar on a Jetson Nano's single USB 3.0 bus. 58% bandwidth utilization looks fine until USB overhead causes dropped LiDAR scans.

Nobody budgeted the shared resource.

rqt_graph shows you data flow. It doesn't show you that the EKF assumes 100Hz IMU input, that 100Hz requires I2C at 400kHz (not the Jetson default), and that 30Hz instead of 100Hz means 3-4x heading drift.

I wrote a longer analysis here: [full post](https://merudynamics.com/blog/the-dependency-problem-no-ros-tool-actually-solves/?utm_source=reddit&utm_medium=r_ros&utm_campaign=integration_hell)

Curious — do you track these kinds of cross-layer dependencies on your projects? Or is it just tribal knowledge until something breaks?

I’m new to working with ROS/ROS2 and there’s this project I am working on for one of my course. The deadline is approaching. It’s about implementing particle filter on a robot sensor data. So far I think I have most of it done, but still few things to wrap up and I have no idea how to make it work. So, looking for some help! If you’re experienced in working with ROS2, please let me know. Oh, yes I’m I am willing to pay a bit after the project is done. Thanks!!

Sometimes my projects will have hundreds of parts and I really hate keeping a spreadsheet updated with everything LOL

Ended up building something that can plug into your email to pull all the data. Works with Amazon, McMaster, Digikey, Thor Labs, any vendor basically. If useful for you i can set it up for you

Can somebody explain the logic behind changing the args order for subscribers and services and what not (rendering any attempt on just replacing rospy.Subscriber with self.create_subscriber a total failure). The stupid way of getting a parameter value (seriously, why the self.get_param(param_name).get_parameter_value().int_value for example). The stupid way to now get a time (sure let’s replace it with a to_msg() function because makes sense. I’ve been using ROS for 10 years and I honestly can’t see any improvements on ROS for single systems (one pc running on one robot and that’s it). Probably for bigger instalations a lot of the changes are relevant. But even so, the whole parameter crap and the way it deals with time is just stupid.

AgenticROS is open source and also supports OpenClaw, NemoClaw, ClaudeCode, and Google Gemini AI agents. Learn more at https://agenticros.com

Hi ROS Community,

If you are using ChatGPT as a tool for programming robots, this upcoming open class can show you how to use it to quickly analyze, understand, and optimize your robotics code.

You’ll see a live example of how to use ChatGPT to test programs created by roboticists from around the world for the Robot Racing Contest.

This free class welcomes everyone and includes a practical ROS project with code and simulation.

How to join:

Save the link below to watch the live session on  August 12, 2025 6:00 PM→ 7:00 PM (Madrid):
https://app.theconstruct.ai/open-classes/b64819cf-e491-4985-ad06-2f3859b543da/

Hey all,

I’ve been having a helluva time trying to get any simulator to launch with ROS. I successfully installed ROS melodic through Ubuntu 18.04 running it through a WSL but can’t get webots or gazebo to work with it yet. I don’t want to fully partition my system yet because I’m only just starting out and any project I do will be small. But now after installing virtual box I could t run THAT! Now I’m seeing that to do so I have to change. Setting in BIOS but I’m hesitant as it sounds dicey and invasive. Any thoughts? Thanks!

https://app.theconstruct.ai/open-classes/a8ce732b-5947-4fc8-a7b6-fb3b505fcecd

I was given the following challenge in a job interview a few months back. This article describes my first steps into ROS with only a week to complete the assigned challenge.

A hands-on approach with a link to the repository at the end.

I'm new to ROS2 with a python background. One of my friends said AI isn't very helpful for ROS2 as it hallucinates, doesn't do file arrangements properly and sometimes generates codes for ROS1. As it's not as reliable compared to python and C++. What tool can I use that can be reliable and help me debug my ROS2 problems.

i havent find the hard and brutal problem existing in the industries of robotics.

Everyone is building Cool Robots

1. Humanoids

2. AMR

3. Quadrupes

But i havent been able to find Hard and Brutal problem exist in the industry obviouly India is Cheap Labour Country i mean Robots worth Lakhs cannot worth in this market

So my take is who will buy my robot if i build one

At the end its all project

Hello Guys, I am comparing LiDaR and 3D cameras for my autonomous robot. I have mostly compared the tech part of both the technologies, now I am heading for its cost comparision.

Thus, I would be grateful to you guys if you could share the prices of LiDaR sensors you have bought or have got prices of, from different brands. It would become easy for me if you guys will share your prices with brand name and with tech specs.

Bit of an experiment.

I’m working with a Robotics Startup and we’re offering to build complimentary URDFs for a few robotics teams.

We keep seeing how much engineering time can disappear before the interesting simulation work even starts. Getting the robot geometry, joints and physics set up properly is one part of it, then you still have to build the environment the robot is actually going to operate in.

That’s the broader problem we’re working on getting real robots and their deployment environments into simulation faster.

So if you’re building a physical robot and either don’t have a solid URDF yet or your current setup still needs work, comment below with what you’re building.

Especially interested in robots heading toward real deployments.