Exploring a human-reviewed ROS 2 agentic pipeline generator

I am prototyping **EdgeAI Forge**, a local-first engineering architecture intended to help generate, test, benchmark, and document ROS 2 and machine-vision pipelines.

The initial target workflow is a ROS 2 vision pipeline generator. Given a requirement such as:

> Read frames from a USB camera, run object detection, publish detections, expose health status, benchmark performance, and prepare deployment to Jetson.

the system should eventually produce reviewed artifacts including:

- package manifests and directory structure;

- Python or C++ nodes;

- topics, messages, services, and actions;

- parameters and launch files;

- unit, integration, and launch tests;

- simulation or recorded-data validation;

- Docker/Jetson packaging;

- FPS and latency benchmarks; and

- assumptions, limitations, and operating documentation.

The current proof of concept contains Planner, Vision, and ROS prompt agents backed by a local Ollama endpoint. It generates design output, not production-ready ROS packages. A broader scaffold contains API, dashboard, infrastructure, and observability components.

The safety boundary is important: generated robot-motion or production-deployment artifacts should never run automatically. The intended sequence includes simulation, test evidence, human review, explicit approval, logging, and rollback.

I would appreciate feedback from the ROS community on:

1. Which package archetype would make the best first supported template?

2. How should generated packages be evaluated beyond compilation and linting?

3. Which launch_testing, rosbag, Gazebo, or other simulation patterns should be mandatory?

4. How should the generator encode QoS, lifecycle nodes, diagnostics, and hardware assumptions?

5. What safeguards would make this useful without encouraging unsafe deployment practices?

Project: GitHub - afridali123/EdgeAI_Forge · GitHub

Background: EdgeAI Forge: My Journey Toward a Local Agentic AI Platform for Industrial Automation

The project is early, and I am sharing it to collect design criticism before implementing the deeper ROS workflow.

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by Afrid_Thenebanda on August 09, 2026 11:58 PM

Some Knowledge about ros

from ROS Discourse General

Hi everyone!

I’m a 2nd-year Mechanical Engineering student at NIT Jalandhar, currently exploring Robotics and Automation.

I’m starting my journey with C++/Python, electronics, CAD and eventually ROS 2. My long-term goal is to work on real robotics projects and eventually pursue research/internship opportunities in robotics.

I’m looking to connect with:
• Students who are also learning Robotics/ROS 2
• People working on interesting robotics projects
• Researchers/engineers who are open to collaboration or guidance
• Students who have previously pursued robotics research internships

If you’re on a similar journey, I’d love to connect and learn together.

Also, if there are any beginner-friendly open-source robotics projects where a student can contribute, I’d really appreciate suggestions.

Thanks!

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by Himanshu1 on August 09, 2026 11:58 PM

Robotics Student - Perception Systems Question

from ROS Discourse General

Hi all, I’m an MEng Robotics & AI student at UCL working on perception/tracking pipelines (recently built a person re-ID and tracking evaluation pipeline, and a Gaussian-splat reconstruction quality evaluator). I’m trying to understand a specific problem better: how do teams currently notice when a perception or sensor-fusion stack has silently degraded in production, before it causes a visible failure? Do you rely on manual spot-checks, logging + alerts, a formal calibration schedule, or something else? Genuinely trying to learn what’s actually painful here vs. what’s already a solved problem, not selling anything. Would appreciate any war stories, even short ones.

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by jeeeeeeeson on August 09, 2026 11:58 PM

Advice on breaking into US robotics/embedded industry as a US citizen with no US history

from ROS Discourse General

I’m a 4th-year Computer Engineering student in Palestine (Birzeit University, graduating 2027). I focus on embedded systems and robotics. I have US citizenship, but I never lived or worked in the US. There’s no embedded or robotics industry here at all, so I can’t really build experience or a network locally. I’m trying to move to the US, but I’m not sure where to start.

My background: ROS2 control software for a multi-agent robotics platform, FreeRTOS firmware on custom boards, and a competition robot (WRO Future Engineers) that I built from the PCB up to the low level firmware. I’m fine with relocating, that’s not a problem for me.

I’m not sure what the right next step is. Should I look for a job directly, try to get into a US grad program first, or something else? If you work in robotics or made a similar move yourself, what would you do in my place?

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by abd_SH on August 09, 2026 11:57 PM

ROS2 Dev Container Feature + Workspace Update

from ROS Discourse General

I’ve been working on a ROS2 Dev Container Feature and just updated my VSCode ROS2 Workspace Template to use it instead of my pre-built ROS Docker images.

The feature is here:

Source:

The main thing I wanted was to select the ROS distro once in devcontainer.json and have both local development and CI use that same configuration.

" Package devcontainers/ros2 · GitHub ": {

“distro”: “lyrical”,

“package”: “desktop”

}

I also cleaned up a bunch of the old X11/WSLg configuration. WSL2 GUI support now works through the normal VS Code Dev Container setup, and native Linux X11 support is available as a separate opt-in feature.

If you’re using ROS2 with Dev Containers, I’d be interested to hear how it works for your setup!

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by athackst on August 08, 2026 04:26 PM

Has a CPU shared-memory backend for rosidl::Buffer been explored?

from ROS Discourse General

I have been looking into rosidl::Buffer and the current buffer backend support, particularly for large variable-length payloads such as images and point clouds.

One idea I am interested in is a CPU shared-memory backend for rosidl::Buffer.

I realize that this overlaps to some extent with functionality that already exists at lower layers. For example, some middleware / RMW implementations already provide shared-memory transport or other zero-copy optimizations. Depending on the implementation, moving data through shared memory may therefore already be possible without introducing a dedicated Buffer backend.

What I am trying to understand is whether there is still a useful role for shared memory at the rosidl::Buffer layer.

My interest is specifically in large variable-length fields where avoiding copies of the payload itself is useful. Rather than treating shared memory only as a transport optimization for a serialized message, a Buffer backend could potentially make the payload storage itself shared and let a buffer-aware RMW transport a descriptor or reference when appropriate.

Conceptually:

ROS message
  metadata
  rosidl::Buffer<uint8_t>
          |
          +-- CPU shared-memory backend
                  |
                  +-- shared payload storage

This seems potentially complementary to transport-level shared memory rather than necessarily a replacement for it. On the other hand, I can also imagine that the overlap with middleware-native SHM mechanisms may be a reason why such a backend has not been pursued.

In particular, I would be interested in hearing:

Has anyone already explored a CPU shared-memory backend for rosidl::Buffer?
Is sharing the backing storage of large variable-length payloads considered an intended use of the Buffer backend abstraction?

Before prototyping something in this direction, I wanted to check whether there is already related work or design discussion that I have missed. I previously explored a similar problem for CUDA IPC in this discussion, so I would especially like to avoid independently reimplementing something that is already being worked on elsewhere.

Work remaining

There is still time left in the GSoC period. The main remaining work is:

Turn repeated run aggregation into a supported command that validates matching runs, excludes warm ups, records its sources, produces median results, and communicates variability clearly.
Handle incomplete upstream output, including non finite values, missing latency files, unreliable message counters, multi-process synchronization, and pinned submodule revisions.
Make result activation and archiving safer, and move duplicated knowledge about benchmark directory layouts into shared metadata.
Fix Kilted support, improve setup documentation, and test the complete workflow on clean machines.

Support for rclpy, hosted automation, CI benchmarking, hosting under performance.ros2.org and a small local graphical launcher are possible stretch or post GSoC efforts. The CLI workflow would remain the main implementation so the project stays scriptable and reproducible.

I would value your feedback

If you work with ROS 2 performance, maintain a client library or middleware implementation, or are simply curious about how two ROS versions compare, please try the public dashboard. I would especially like to know whether the overall result makes sense without prior benchmark knowledge, whether you can find the scenario behind a warning, and what information would help you trust or question a comparison.

Feedback from both experienced ROS developers and people seeing these measurements for the first time would be very useful and appreciated from the depths of my heart. Thanks for reading.

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by ammaarrahmed on August 06, 2026 08:16 PM

From URDF to SimReady: What a Robotiq Gripper Taught Us About Simulation Assets

from ROS Industrial

For industrial robotics teams, simulation is useful only when the important parts of the simulated system behave enough like the real system to support better engineering decisions. A robot arm that looks right but reaches to the wrong pose is an obvious problem. A gripper that looks right but responds differently during contact can be harder to notice, and in manipulation work it may matter even more.

At Southwest Research Institute, we have been developing and evaluating Physical AI approaches for high-mix manipulation. The broader effort combines teach-through-demonstration with simulation, with the goal of supplementing demonstrations through reinforcement learning. Our lab setup includes Universal Robots UR5e arms and Robotiq 2F-85 grippers. The full workcell matters, but the gripper became the clearest example of a practical problem many robotics teams are beginning to face: the asset that is available is rarely the same thing as the asset that is ready for production-oriented simulation.

Isaac Sim representation of the lab system

The first problem: available assets are not automatically usable assets

A common starting point in a simulation project is to use the assets that are already available in the simulator or in the ROS ecosystem. That was our starting point as well. Isaac Sim includes Robotiq gripper assets, and there are public Robotiq-related resources in the ROS and ROS 2 ecosystem. Those resources are valuable, but in our testing they did not immediately give us the behavior we needed.

The issues were not cosmetic. We encountered practical asset-structure and behavior problems, including difficulty restructuring an articulation root, gripper assets that were not instanceable, and contact behavior that failed when a mimic joint encountered an object asymmetrically at one finger pad. Each of those issues matters in a production-oriented simulation workflow.

For readers who do not spend their day in USD internals, an “instanceable” asset is one that can be referenced and reused cleanly rather than copied and manually modified each time. That matters when a simulated workcell becomes more complex or when the same asset must appear in many scenes. A “mimic joint” is a joint whose motion follows another joint. For a mechanically coupled gripper, mimic behavior can be the right abstraction because the physical hardware is not simply two independent fingers driven by unrelated commands.

That distinction is central to the Robotiq 2F-85.

Why the Robotiq 2F-85 is a useful test case

The Robotiq 2F-85 is a parallel gripper, but its mechanism is not as simple as two independent pads moving toward each other. It includes closed-loop mechanical behavior, which creates challenges for simulation asset authoring. The practical modeling decision becomes: should the gripper be represented with one driven joint and mimic behavior, or should both sides be driven independently?

A single driven joint with mimic behavior is attractive because it better reflects how the gripper is normally commanded. Driving both sides independently can make an asset move in simulation, but it introduces extra controller and joint-state complexity and moves the simulation farther away from the real gripper abstraction. We evaluated that path by importing a URDF into USD and adding drives to both sides. For our purposes, that direction created more complexity than value, and it likely would still have required mimic behavior to represent the coupled mechanism faithfully.

NVIDIA’s Isaac Sim documentation includes a tutorial on rigging closed-loop structures using a Robotiq 2F-85 gripper, and that tutorial points to a workflow that starts from a CAD/Onshape representation and then adds the physics, joint, and drive configuration needed to make the asset functional in Isaac Sim. That detail is important: import is only the beginning. A realistic gripper asset still needs careful authoring and validation.

What we tried from the ROS ecosystem

After our initial asset testing, we reviewed several public resources related to Robotiq grippers, including the older ROS-Industrial Robotiq repository, PickNik’s ros2_robotiq_gripper, and UW-Lab resources and assets. It’s worth noting that Robotiq does not currently provide first-party assets for its grippers; all of the resources we tested are community-maintained.

The best-performing candidate in our lab testing was the UW-Lab calibrated USD asset for the Robotiq 2F-85. That asset appears to follow the same general pattern as the Isaac Sim closed-loop structure workflow, with additional modifications. Out of the box, it behaved better than the other candidates we tested. Even though it uses mimic behavior, it did not break when an object contacted one finger before the other, and we did not observe the unexpected mesh behavior we saw elsewhere when larger forces were applied.

Simulated Robotiq 2F-85 mounted on the UR arm

That made the UW-Lab asset a much better starting point. It did not make the problem disappear.

The remaining fidelity gap

The physical Robotiq 2F-85 still exhibited behavior that the simulation did not capture. In the real gripper, when an object is grasped near the base-side region of the finger pads, the pads can angle inward slightly. When the object is grasped farther out on the pads, the pads remain parallel.

In our simulation, that behavior was not represented. The simulated kinematics and physics did not capture the same pad motion we observed on the physical gripper.

That may sound like a small difference. In manipulation, small differences at the contact interface can become large differences in outcome. A grasping policy trained or validated in simulation is sensitive to contact geometry, friction, compliance, joint coupling, and failure modes. A gripper asset that works for visualization may still be insufficient for reinforcement learning, synthetic data generation, or pre-deployment validation.

The broader lesson: conversion is not fidelity

ROS users tend to start with URDF, and for good reason. URDF is familiar, widely supported, and often the most available robot description format for ROS-based systems. SDF is also common in simulation workflows. USD and OpenUSD offer a powerful scene representation for modern simulation and digital-twin workflows. But moving from URDF or SDF to USD does not automatically create the physical and behavioral information needed for high-fidelity simulation.

A converter can translate what is present. It cannot reliably invent what is missing.

That is where the SimReady idea is useful. NVIDIA describes SimReady as more than placing simulation data into a USD file. The goal is to represent simulation-ready content through named, typed, validated properties that tools can interpret, validate, and use. In NVIDIA’s broader description, SimReady assets include physics properties, semantic labels, material attributes, and, where needed, behavioral or articulation data.

For robotics teams, that framing exposes the real gap. A useful production asset is not merely a mesh. It is not merely a URDF. It is not merely a USD file. It is a validated representation of geometry, kinematics, dynamics, contacts, materials, semantics, and control-relevant behavior at the level required by the task.

Practical takeaways for robotics teams

First, validate assets against the behavior that matters for the application. Loading the asset, moving the joints, and rendering the workcell are necessary checks, but they are not enough. For manipulation, validation should include contact cases, asymmetric grasps, edge grasps, expected failure modes, verification of the mesh geometries, and comparisons against the physical hardware.

Second, choose the simulated command abstraction deliberately. For a mechanically coupled gripper, independent finger drives may be convenient during asset authoring, but they may also create a mismatch with the real system. If the physical gripper is commanded as a coupled mechanism, the simulation should preserve that abstraction unless there is a clear reason to do otherwise.

Third, track asset provenance and simulator version. Isaac Sim documentation, import workflows, asset structure, and tuning parameters can vary between versions. A gripper that behaves acceptably in one workflow may require different configuration in another. Asset source, simulator version, import method, and post-import modifications should be captured as part of the engineering record.

Fourth, treat conversion as the start of an asset-authoring workflow rather than the end. URDF-to-USD or SDF-to-USD conversion is valuable, but high-fidelity simulation still requires authoring, tuning, and validation. The missing information often lives with the equipment manufacturer or must be measured experimentally.

Finally, involve equipment manufacturers where possible. Manufacturers are often best positioned to provide richer kinematic, dynamic, material, and behavioral information about their products. The robotics community would benefit from a more standard way to move that information from manufacturer data into ROS-compatible descriptions, USD-based simulation assets, and validation tests.

Toward a better ROS-to-SimReady workflow

The Robotiq 2F-85 experience points to a larger opportunity for the ROS-Industrial, open-source robotics, simulation, AI, and equipment-manufacturer communities. We need workflows that preserve what ROS users already rely on while adding the physical and behavioral fidelity required by modern high-fidelity simulation.

A practical workflow could look something like this:

Manufacturers provide CAD, URDF or SDF descriptions, kinematic details, material properties, actuator behavior, and validation data;
ROS and open-source tools support accessible robot descriptions and integration;
USD-based simulation workflows support composition, reuse, and high-quality scene representation; and
SimReady-style validation defines whether an asset is ready for the intended class of simulation tasks.

The important point is that “simulation ready” should become an engineering claim that can be tested, not a label applied because an asset loads in a simulator.

There are signs this is starting to happen. When we contacted Robotiq prior to publication, they indicated that “an official C++ SDK, ROS 2 driver, URDF and updated Isaac Sim assets are in active development.”

Conclusion

Our experience with the Robotiq 2F-85 was a reminder that the hard part of simulation is not always the robot arm, the environment, or the renderer. Sometimes the hard part is the contact behavior of a gripper pad at the exact point where the simulated world meets the physical one.

URDF, SDF, USD, and SimReady all have roles to play, but no single file format solves the fidelity problem by itself. For production robotics, a simulation asset earns trust only when it reproduces the behaviors that affect the task. The closer the ROS, simulation, AI, and equipment communities can align around that standard, the less time teams will spend rebuilding the same assets and the more confidence they can place in simulation before deploying to real hardware.

by Jerry Towler on August 05, 2026 09:02 PM

Last day to purchase regular price ROSCon Global tickets is Monday, August 24th

from ROS Discourse General

Hi Everyone,

Quick reminder, the last day to purchase regular price tickets for ROSCon Global in Toronto is Tue, Aug 25, 2026 6:59 AM UTC.

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by Katherine_Scott on August 04, 2026 09:59 PM

Announcing protoros2: use protobuf in ros2 without compromise

from ROS Discourse General

Hi ROS Community!

For years, there has been a strong and consistent demand for seamless Protobuf serialization in ROS/ROS 2. While there are excellent existing tools in the community, integrating them cleanly into a high-performance, production-ready pipeline often comes with friction.

Today, we’re excited to introduce protoros2 — a middleware wrapper and orchestration engine designed to provide zero-intrusive protobuf support for ROS 2. ZhenshengLee/protoros2: use protobuf in ros2 without compromise

(The name is heavily inspired by the awesome flatros2 GitHub - Ekumen-OS/flatros2 · GitHub project!)

What does protoros2 do?

protoros2 does not reinvent the wheel. Instead, it acts as a non-intrusive “Tri-State Orchestration Engine” that elegantly binds third-party Open-Source foundations into a unified architecture. It allows you to use Protobuf in your stack without compromise.

It provides an out-of-the-box EnterpriseNode wrapper that offers multi-channel communication:

• Proto Channel (Zero-Intrusive Fast-Path): Transparently inspects the underlying RMW serialization format at runtime. If the RMW supports Protobuf natively, it routes messages directly via rclcpp::SerializedMessage. If not, it gracefully falls back to standard CDR via rclcpp::TypeAdapter.
• Flat Channel (Performance Bonus): An optional bypass channel optimized for ultra-low latency IPC (powered by Iceoryx shared memory), fully adapted to the ROS 2 executor ecosystem.

Key Features & Use Cases

We designed protoros2 to be flexible enough to accommodate different team workflows, supporting multiple “Single Source of Truth” (SSOT) architectures seamlessly:

• Use Case A: Standard ROS 2 .msg as SSOT
Write your standard .msg files as usual. protoros2 works seamlessly with standard RMWs (CDR only) or native Protobuf RMWs without altering your application logic. In fallback modes, it can even handle simultaneous CDR and Protobuf topic ecosystems flawlessly.
• Use Case B: AI/Robotics .proto as SSOT
For AI-first teams, define your data structures natively in .proto. protoros2 can either co-exist with a generated mirror IDL or operate purely on .proto, bypassing .msg entirely for a direct, zero-overhead binding.
• Native ROS 2 Executor Support:
Whether you prefer standard Callback Push, WaitSets, CallbackGroups (Mutually Exclusive/Reentrant), Polling Subscribers, or Intra-Process Comm—protoros2 natively integrates these paradigms out of the box.
• MLOps Ecosystem Ready:
Full compatibility with mcap format and rosbag2 plugins. Data scientists can consume protobuf bags directly with native python bindings.

Enterprise Security Built-in

To ensure consistency in large-scale deployments, protoros2 utilizes strict C++ access controls to safely encapsulate the raw rclcpp::Node. It intercepts and disables risky dynamic ROS 2 configurations (like parameter services and QoS overriding) at compile time, guaranteeing predictable behavior on the vehicle edge without sacrificing the standard ROS 2 developer experience.

Acknowledgement

This work stands on the shoulders of giants. We want to express our deepest gratitude to the following incredible projects and their contributors, without which protoros2 would not have been possible:

• rosidl_typesupport_protobuf https://github.com/eclipse-ecal/rosidl_typesupport_protobuf: For providing the robust C++ TypeSupport handle and TypeAdapter generation engine.
• proto2ros https://github.com/rai-opensource/proto2ros: For the brilliant AST parser bridging .proto definitions to synthetic IDL .msg.
• ros-central-registry https://github.com/intrinsic-opensource/ros-central-registry/blob/main/examples: For the excellent Bazel + ROS 2 integration examples and Protobuf C++ references.

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by ZhenshengLee on August 04, 2026 07:11 AM

Boston Robot Hackers announces August Monthly Meeting

from ROS Discourse General

Boston Robot Hackers is pleased share info about our August meeting:

Topic: Forward & Inverse Kinematics: The Math: From joint angles to end-effector poses"
Speaker: Shivam Chopra, PhD
Date: August 6 2026
Time: 7:00pm to 9:00pm
Location: Artisans Asylum, Alston, Boston

Shivam will introduce the concepts of Forward and Inverse Kinematics, explain where it fits into robotics (and how important it is!) and get into technical details of how to apply it and how the math works.

Also featured two lighting talks

The Pupper Project team will give an update of their progress and hopefully show off their puppy
The Beaver Works Summer Institute will present highlights of this summerâ€™s program

PLEASE REGISER! brh.eventbrite.com

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by pitosalas on August 04, 2026 12:49 AM

ADEL 2.0 – C++/Rust Deterministic Execution Layer for Microsecond Edge Compute & Robotics

from ROS Discourse General

Hi ROS Community!

We are opening early-access evaluations for ADEL 2.0 (Autonomous Deterministic Executive Layer), a bare-metal C++/Rust execution engine built for ultra-low latency, zero-jitter control loops.

While designed for microsecond satellite maneuver planning under severe compute bounds, ADEL 2.0 provides immediate utility for terrestrial robotics, BVLOS drone flight controllers, and autonomous hardware running ROS/ROS2 node topologies.

Key Highlights:

Microsecond-latency spatial trajectory recalculation.
Deterministic, zero-jitter C++/Rust core execution loop.
Lightweight memory footprint suitable for embedded edge targets.

Interactive Monitor & Visualizer:

We are actively scheduling 15-day to 30-day Hardware-in-the-Loop (HIL) benchmarking pilots with robotics hardware teams and autonomous system integrators.

Feel free to test the live monitor above or reach out at annesham649@gmail.com if youâ€™d like to benchmark ADEL 2.0 against your current ROS control stack!

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by Annesha_Mazumdar on August 03, 2026 04:08 PM

ROS 2 Realtime Support Package

from ROS Discourse General

We have been working on adding real-time functionality to rcl and rclcpp since 2022.
In response to this proposal, we have created a new package that provides real-time functionality without changing the existing packages.

esol-community/ros2_realtime_support

Background

The previous discussion is as follows: ROS lacks a unified mechanism to formally support real-time functionality, and we have tried to achieve this by adding functionality to rcl and rclcpp.

On the other hand, it has been pointed out in past PTCs that it makes it difficult to check at release time and to support the OS.
Since CallbackIsolatedExecutor was announced around the same time, we have also changed our policy to provide functionality in separate packages.

Update REP-2017 prototype and executor using OS native threads - ROS/ROS General - Open Robotics Discourse

How to use

For now, we provide rclcpp-friendly classes. There are four things to do:

Add package description to CMakeLists.txt and package.xml
Add a configuration file in YAML format
Change the main routine in the source file
- Change rclcpp::init, rclcpp::shutdown to the rclcpp_realtime namespace
- Change executors to be able to apply thread attributes provided by rclcpp_realtime

This allows thread attribute settings to be applied to executors by specifying an environment variable or a configuration file with --ros-args.
See the README below for details.

ros2_realtime_support/examples_rclcpp_realtime/README.md at rolling · esol-community/ros2_realtime_support

Discussion & Future Work

Despite the name, real-time support, not much has been done.
The thread attributes can be managed through the extended rcl interfaces; APIs for thread operations, abstracted by these attributes, are provided, and executors that use the attributes have been added.

In the future, we will change the mutexes and condition variables used in rclcpp_realtime on an RTOS to call OS-native APIs.
Another challenge is to make intra-process communication real-time, so we can guarantee real-time performance in robot systems that run on a single PC.

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by k-yokoyama-esol on August 02, 2026 01:21 PM

Uv on ROS 2: a field report on workspace-level virtual environments — five failure modes and minimal colcon/ament proposals

from ROS Discourse General

TL;DR: A workspace-level uv-managed venv works on stock apt-installed ROS 2 — including a PyTorch+CUDA node — but we hit five reproducible failure modes on the way (verified on Jazzy; none of the mechanisms are Jazzy-specific). Key measurement: the known shebang workaround ([build_scripts] executable = /usr/bin/env python3) does not cover --symlink-install, so when colcon is launched from the system Python there is currently no complete workaround. Below are four minimal change proposals for colcon/ament — all opt-in, none fixing the venv’s location or name, with no behavior change for workspaces that do not use a venv.

Background

PEP 668 disabled pip install into the system Python on Ubuntu 24.04, and deep-learning robotics often needs exact version pins and custom package indexes (e.g. torch==2.6.0+cu124) that package.xml/rosdep currently has no way to declare. A per-workspace virtual environment with pyproject.toml and a lockfile — managed here with uv — is one practical answer. In Letting Python Be Python, the idea that workspaces could become venvs was raised, along with the question of what it would take to get there; Status of Colcon building “standards-based” Python packages covers the related build-tool work. This post adds empirical data to that discussion: we migrated a real robot stack to uv while keeping colcon, ros2 run, and ros2 launch in use, and recorded what broke and why.

What works and what breaks

With a venv created by uv venv --system-site-packages from the distro interpreter, and python-preference = "only-system" set in the [tool.uv] section of pyproject.toml, everything builds and a torch+CUDA inference node runs on the venv’s Python, with lockfile reproducibility and custom wheel indexes.

Setup: Ubuntu 24.04 / apt Jazzy / Python 3.12.3 / setuptools 68.1.2 / uv 0.11.28, and pyproject.toml (click for more details)

Along the way we hit five reproducible failure modes. All of them can be worked around, but the workarounds are not covered by official documentation, so they are easy to rediscover independently:

#	Failure mode	Cause	Current workaround
1	Every shell needs two setup steps (`source install/setup.bash` and venv activation), in order	The ROS environment and the venv have no knowledge of each other	Hand-written shell setup per project
2	colcon treats directories inside the venv as packages during discovery	Package discovery descends into every subdirectory	`touch .venv/COLCON_IGNORE` (documented)
3	`ros2 run` executes ament_python nodes with the system interpreter even while a venv is active	colcon runs `setup.py` with its own `sys.executable`; setuptools writes that interpreter into console-script shebangs	Incomplete — see next section (ros2/ros2#1094, open since 2021)
4	numpy 2.x in the venv breaks apt-built extensions (cv_bridge) at import	Jazzy binaries are built against numpy 1.26’s C ABI	Pin `numpy<2` in the workspace
5	uv provisions its own standalone CPython, which mismatches distro-built C extensions	uv’s default `python-preference`	`python-preference = "only-system"` in pyproject.toml (`[tool.uv]`)

The remaining gap

Four of the five have complete workarounds; #3 does not. A known mitigation is [build_scripts] executable = /usr/bin/env python3 in setup.cfg (mechanism related to colcon-core#183, reported in ros2/ros2#1094). We measured it on Jazzy:

Regular colcon build: works — scripts get env shebangs and resolve to the active venv.
colcon build --symlink-install: not applied — the develop/editable code path keeps #!/usr/bin/python3, so the mode commonly used during development is not covered.
Launching colcon from the venv itself — .venv/bin/python -m colcon build — covers both modes (with --system-site-packages, the apt-installed colcon is importable from the venv, so nothing extra needs to be installed). The limitation: the venv’s absolute path is written into the generated shebangs, so the result does not survive venv recreation and install/ is not relocatable.

Bottom line: when colcon is launched from the system Python — the common configuration in tutorials and CI — there is currently no complete workaround.

Proposed changes

One design principle for all four: opt-in, no fixed venv location or name, and no behavior change for workspaces that do not involve a venv.

P1 — discovery: skip any directory containing pyvenv.cfg (the PEP 405 marker every venv has) during package discovery — an automatic COLCON_IGNORE for venvs of any name, in any location.
P2 — shebangs: an option to emit #!/usr/bin/env python3 shebangs on both the install and the develop (--symlink-install) code paths. The setup.cfg mitigation covers only the install path and has to be repeated in every package; an option at the build-tool level would cover a whole workspace at once. Where no venv is active, env python3 resolves to /usr/bin/python3 as before.
P3 — activation: record the path of the interpreter colcon built with under install/, and let setup.bash read it and, if that interpreter belongs to a venv, activate it (with an opt-out environment variable). This is a minimal mechanism for the “workspaces as venvs” idea from the threads above, and it leaves the venv’s location entirely up to the user.

Relation to existing work

colcon-uv manages Python dependencies per package, installed during colcon build. This post focuses on one environment and one lockfile per workspace; the two granularities address different needs (per-package isolation vs. one shared environment for a whole launch graph) and can coexist.
Pixi as a co-official installation method concerns how ROS itself is installed. The scope here is different and does not compete with it: keeping the standard apt installation and making the Python layer of one workspace reproducible.
ros-python-wheels distributes ROS client libraries as pip-installable wheels. The direction here is complementary: using pip/uv-managed dependencies inside a standard, apt-based ROS workspace.
A similar uv setup (--system-site-packages + lockfile) has been shared in Status of Colcon building “standards-based” Python packages, with nodes started directly through python. The measurements above cover the case where colcon, ros2 run, and ros2 launch stay in use.

Open questions

For P1: would a package-identification extension in colcon-core, modeled on the existing COLCON_IGNORE one, be an acceptable shape — or would this fit better as a separately distributed extension package?
For P2, which layer would be better suited to handle the develop-path shebang — colcon-core, or the setuptools develop machinery?
For those running workspace-level venvs with colcon in CI or on production robots: which failure modes are missing from the list above (overlays, cross-compilation, non-Ubuntu platforms)?

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by Decwest on August 02, 2026 09:48 AM

VectorField Planner: 7 µs global path queries with strict optimality — REST API for occupancy grids, Nav2 plugin on roadmap

from ROS Discourse General

Hi all,

I’ve been working on a global planning engine aimed at warehouse/fleet
deployments, and I just opened a free API tier. I’d love feedback from people
running real Nav2 fleets.

What it does

You upload an occupancy grid once. It solves a field for your goal (charging
station, pick station, dock), and from then on every path query — from any
start cell — returns a strictly optimal path in microseconds, without
re-searching the map.

The pitch for fleet operators: the cost of global planning stops scaling with
the number of robots.

Measured numbers (C++ core, single thread, low-end 2-core CPU)

1M-cell 3D warehouse map (100³, mezzanine floors + rack walls):

Metric	VectorField	A* (C++, typical)
One-time solve per goal	47 ms	—
Query, any start pose	7 µs	~5 ms, every query
Optimality	1.0000 (BFS-verified)	optimal
Peak memory	5 MB	—
10,000 simultaneous queries	70 ms total	~50 s

Dynamic sites: obstacle removal (cleared shelves, opened gates) is patched
exactly, 5.9× faster than a rebuild, zero error. Every solve is a fixed,
bounded number of identical array operations, so worst-case latency is known
in advance — relevant if you need timing guarantees for certification.

Where this fits in a ROS stack

Input: an occupancy grid (the same data you already publish on /map or
keep in a costmap_2d layer)
Output: an optimal cell path per query
Today: plain REST API, so anything that can HTTP can plan. A native Nav2
global-planner plugin (drop-in replacement for Navfn) is on the roadmap —
the field-reuse model maps nicely onto multi-goal / fleet planners, which is
exactly where Navfn recomputes the most.

Typical integration I’m picturing: your fleet manager uploads the map once per
shift (or per layout change), then every robot’s global plan request is a
~7 µs lookup instead of a Navfn re-search.

Honest limitations

Built for structured, mostly-static environments — warehouses, factories,
indoor drones. Not for highly dynamic unstructured spaces.
Obstacle insertion currently uses a repair fallback; exact fast insertion is
roadmap work.
It’s a hosted API (with an on-prem license option), not an open-source
package. Free tier is genuinely free: 100³ maps, 1,000 solves + 100K
queries/month.

Links

Live browser demo (races the solver against A*, no sign-up):
https://vectorfield.top
Free API key (instant): https://vectorfield.top
API docs: VectorField Planner API - Swagger UI

Questions I’d especially love feedback on:

For those running multi-robot fleets: how do you handle global replanning
cost today? Is 5 ms/query/robot actually hurting you, or is local planning
the real bottleneck?
What would a Nav2 plugin need to do for you to consider it (topic/action
interface, costmap update cadence, multi-goal support)?
Any interest in an on-prem / offline deployment for sites without
connectivity?

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by ShiyanPeng on July 31, 2026 05:02 PM

A new tool to create ros2 package with executables, c++ and Python node in one package

from ROS Discourse General

site: GitHub - yjphhw/ros2_pkg_create: A utility script for generating ROS 2 package templates that support both C++ and Python nodes, simplifying mixed-language development within a single package. · GitHub

useage is very easy, just download the ros2_pkg_create.py and put in a workspace(direction),

and run

python3 ros2_pkg_create <package_name>

such as create demo_pkg:

python3 ros2_pkg_create demo_pkg

output is :

正在生成混合功能包: my_pkg
已创建: src/my_pkg/CMakeLists.txt
已创建: src/my_pkg/package.xml
已创建: src/my_pkg/setup.cfg
已创建: src/my_pkg/LICENSE
已创建: src/my_pkg/src/hello_world.cpp
已创建目录: src/my_pkg/include/my_pkg/
已创建: src/my_pkg/src/script_node.py
已创建: src/my_pkg/my_pkg/init.py
已创建: src/my_pkg/my_pkg/py_node.py

功能包 [my_pkg] 生成完毕！
提示: 记得在 CMakeLists.txt 中根据需要补充依赖项。

按照以下步骤进行下一步操作:
1.编译功能包: colcon build --symlink-install --packages-select my_pkg
2.安装功能包: source install/setup.bash
3.测试可执行程序: hello_world
4.测试 C++节点: ros2 run my_pkg hello_world
5.测试 Python 节点: ros2 run my_pkg script_node
6.测试 Python 模块: ros2 run my_pkg my_py_node

follow the instructions in output, you can build and run package in one minute.

you will get a ros2 pkg template, you can easily add C++ , Python and normal executable.

welcom to Star the project: GitHub - yjphhw/ros2_pkg_create: A utility script for generating ROS 2 package templates that support both C++ and Python nodes, simplifying mixed-language development within a single package. · GitHub

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by yjphhw on July 31, 2026 01:29 PM

How do you understand the architecture of a large ROS 2 workspace?

from ROS Discourse General

Hi everyone,

I’m curious about how other ROS 2 developers approach understanding a large or unfamiliar workspace.

When joining an existing project or reviewing a large codebase, I often find myself asking questions like:

Which packages depend on each other?
Which nodes communicate together?
What topics, services, and actions are used?
Are there isolated nodes or communication issues?
Does the implementation still match the intended architecture?
How do you quickly get a high-level understanding before running the system?

I’m interested in learning about your workflow.

For example:

Which tools do you use?
Do you rely mostly on runtime tools such as rqt_graph, Foxglove, or RViz?
Do you have internal scripts or documentation that help?
Do you manually inspect the source code?
Do you perform any kind of static analysis before launching the system?
How do you review architectural changes in CI?

I’m particularly interested in workflows for medium-to-large industrial projects where a workspace may contain dozens (or even hundreds) of packages.

Looking forward to hearing how everyone approaches this problem and what has worked well in practice.

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by aminebensaid66 on July 30, 2026 04:51 PM

Jenkins version upgrade of build.ros2.org [Scheduled Buildfarm Downtime]

from ROS Discourse General

Hello ROS Community,

The OSRF Infrastructure Project is planning to update the Jenkins version of https://build.ros2.org as part of our ongoing efforts to maintain and improve the ROS buildfarm infrastructure. To facilitate this migration, the following services will experience downtime during the maintenance window:

https://build.ros2.org (Jenkins) will be temporarily unavailable or in shutdown mode (not running jobs).

The migration is scheduled to begin on Monday Mon, Aug 3, 2026 11:30 AM UTC (11:30 UTC) and is expected to last for 4 hours. During this time, the buildfarm will be offline, and all queued jobs will need to complete before Jenkins is taken offline.

Once the upgrade is complete, I’ll update this thread to confirm that services are back online. I’ll also be monitoring for any issues that may arise as a result of the upgrade.

Thank you for your patience as we work to improve the ROS buildfarm infrastructure. If you have any questions or concerns, please feel free to reach out in this thread.

Att,

Cristóbal

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by Crola1702 on July 30, 2026 04:37 PM

Chinese legged/humanoids banned in USA, what are the alternatives?

from ROS Discourse General

Just saw that newly imported Chinese legged and humanoid robots are now banned in USA, what other alternatives are there? I know Unitree had ROS interface in both Go2 dog and G1 humanoid (and you could jailbreak cheap base version instead of expensive research one).

What other alternatives are there?
Will this spur open source/open hardware design?

Again, Iâ€™m adding poll of what legged/humanoid robots have you used/planned to use

What legged/humanoid robots have you used/will use?

Chinese( Unitree Go2, G1, etc)
Boston Dynamics (Spot, Atlas, etc)
Musk(Optimus)
Figure
Anybotics
Open source/ Open Hardware
Other

Click to view the poll.

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by martincerven on July 29, 2026 08:24 AM

Learning Zenoh: A New Communication Layer for ROS 2

from ROS Discourse General

Hi everyone,

Recently, I’ve been learning more about Zenoh and its role in the ROS 2 ecosystem. Since most ROS 2 applications rely on DDS for communication, I was curious about how Zenoh approaches the same problem and where it can provide advantages.

From what I’ve learned so far, Zenoh offers a lightweight communication layer that aims to reduce latency, minimize bandwidth usage, and simplify communication across distributed systems. These characteristics make it particularly interesting for robots running on resource-constrained hardware such as the Raspberry Pi or for systems that need to communicate across different networks.

I’m currently developing a mobile robot called Pavlov Mini Wheel, based on ROS 2 Humble, and I’m planning to experiment with Zenoh for communication between the onboard Raspberry Pi and an external laptop running perception and navigation workloads. It seem like an interesting opportunity to compare its behavior with the default DDS-based setup.

This post is the first step in my exploraiton of Zenoh. Over the next few weeks, I plan to document:

Installing Zenoh with ROS 2 Humble
Integrating Zenoh into a existing ROS 2 project
Comparing DDS and Zenoh in practical robotics scenarios
Sharing performance observation from a real robot

If you’ve already used Zenoh in your projects, I’d be happy to hear about your experences, recommendations, or challenges you’ve encountered.

My article on the relevant topic:
Medium: https://medium.com/@bengokaysaglam/beyond-dds-introducing-zenoh-for-modern-ros-2-systems-1cacbfcc21f3

Looking forward to learning from the community!

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by bengokaysaglam on July 28, 2026 12:08 PM

YERP + rosbag2 snapshot: can we capture why a ROS perception latency spike happened?

from ROS Discourse General

[Update] Thanks to the great feedback in the comments regarding rosbag2 snapshot, I have updated the original post to clarify how YERP and rosbag2 snapshot work perfectly together as a trigger-and-capture pair!

Hi everyone, Recently I have been thinking about a very practical debugging problem in ROS / ROS2 perception pipelines, especially for AMRs and mobile robots. When a robot behaves strangely in the field, the usual workflow is often:

rosbag record everything.

Bring the massive data back.

Replay it and try to find what happened.

Of course, rosbag2 is powerful. I am also aware of rosbag2 snapshot mode, which can keep recent messages in memory and dump a raw topic window when triggered. That is actually very close to the architecture I have in mind. I do not want to replace rosbag2 or its snapshot mode. Instead, I want to explore a lightweight layer above it: an event-triggered Runtime Evidence Layer for ROS perception pipelines.

I call the current prototype YERP — originally “YOLO Edge Runtime Profiler”.(Project repo: https://github.com/ZC502/yolo-edge-runtime-profiler)*

The core idea

In my view, the layering could be:

rosbag2 snapshot: captures the raw ROS topic window.
YERP / EvidenceFlow: decides when the snapshot is worth triggering, records why it was triggered, and adds structured perception-runtime evidence.

So the question is not: Can YERP replace rosbag2? The question is: Can YERP act as the lightweight anomaly detector / evidence sidecar layer that triggers rosbag2 snapshot at the exact right moment?

For example, a robot may run normally most of the time, but occasionally:

average FPS looks fine, but p95 / p99 latency spikes
the local planner receives perception results too late
a detection frame creates too many candidates
postprocess suddenly dominates runtime
the issue is hard to reproduce later

In that case, raw topic replay is useful, but we also need to know:

Why was this moment captured?
Which frame was involved?
Which perception stage became slow? (preprocess, inference, postprocess, callback delay, queue delay, or message age?)
Did output pressure increase? Was there a box-count or candidate-count spike?

That is the role I am exploring for YERP.

Current prototype status

The current YERP Vision prototype has already been tested in a standalone YOLO / edge CV pipeline. It can monitor fields such as preprocess_ms, inference_ms, postprocess_ms, p50/p95/p99 latency, box count, and confidence entropy.

When a runtime pressure event is triggered, it saves local evidence (image.jpg, metadata.json, etc.).

In one test case, a normal-looking YOLO frame was captured not because it was manually labeled as a “[bad frame] ”, but because the runtime trace showed pressure:

{
  "state": "RED",
  "dominant_cause": "POSTPROCESS_DOMINANT",
  "selection_reason": "runtime_pressure",
  "metrics": {
    "box_count": 18,
    "class_count": 4,
    "confidence_entropy": 2.51,
    "preprocess_ms": 1.20,
    "inference_ms": 3.95,
    "postprocess_ms": 2.02,
    "postprocess_ratio": "28.15%"
  }
}

The important point is not that the image “looks abnormal”. The important point is: this input frame created measurable runtime pressure, so it became worth saving as evidence.

Community Momentum

This concept is already gaining cross-community traction:

I have introduced this EvidenceFlow approach in the Ultralytics GitHub Discussions, where the team is currently gathering feedback.
I’ve also initiated an open co-testing discussion within the MindSpore Lite community, and developers there are exploring collaboration for edge NPU inference.

Now, I want to bring this discussion to the ROS ecosystem, which is arguably where field debugging is the most painful.

EvidenceFlow Schema v0.1 Draft

Instead of exposing all internal YERP logic, I am thinking about a simple structured sidecar format. A ROS / ROS2 runtime pressure event could look like this:

{
  "schema_version": "0.1",
  "record_type": "runtime_pressure_event",

  "sample": {
    "sample_id": "frame_1048",
    "input_ref": "/camera/front/image_raw",
    "timestamp": "2026-07-27T19:32:18.104Z"
  },

  "environment": {
    "runtime": "ros2",
    "backend": "yolo_edge_runtime",
    "mode": "event_only",
    "device": "amr_edge_board"
  },

  "trigger": {
    "state": "RED",
    "reason": "latency_p99_spike",
    "observed_ms": 51.7,
    "threshold_ms": 30.0,
    "dominant_cause": "POSTPROCESS_DOMINANT"
  },

  "stage_ms": {
    "preprocess": 2.1,
    "inference": 15.4,
    "postprocess": 34.2,
    "total": 51.7
  },

  "ros_metadata": {
    "topic": "/camera/front/image_raw",
    "node": "/perception/yolo_detector",
    "callback_delay_ms": null,
    "queue_delay_ms": null,
    "message_age_ms": null,
    "dropped_messages": null
  },

  "output_metadata": {
    "box_count": 145,
    "candidate_count": 312,
    "confidence_entropy": 2.8,
    "class_entropy": 1.4
  },

  "hardware_metadata": {
    "cpu_usage": null,
    "gpu_usage": null,
    "npu_usage": null,
    "memory_spike_mb": 12,
    "temperature_c": null
  },

  "snapshot": {
    "rosbag2_snapshot_triggered": true,
    "window_sec": 5
  },

  "privacy": {
    "local_first": true,
    "image_saved": true,
    "upload_performed": false
  }
}

The goal is not to force every ROS project to use these exact fields, but to discuss what a useful runtime evidence record should contain.

Runtime Overhead

A natural concern is: Will such a probe slow down the robot?
Absolutely valid. A probe should not become the new bottleneck. The design separates the workload:

Fast path (Main inference loop): read existing timestamps, update a fixed-size rolling window, check trigger conditions. No file IO, no large tensor copies, no image saving.
Slow path (Background worker): after a trigger, write JSON / image in a background worker, and optionally call rosbag2 snapshot service. Uses a bounded queue and drops evidence if full to avoid blocking inference.

For production ROS / ROS2 systems, the default modes would likely be shadow_mode (observe only) or (write only when triggered).

How I imagine the rosbag2 integration

Observe: YERP observes perception timing and output metadata.
Detect: YERP detects a runtime pressure event (p99 spike, queue delay, output pressure, etc.).
Log: YERP writes a small EvidenceFlow JSON sidecar.
Trigger: If raw replay is needed, YERP calls rosbag2 snapshot service.
Result: raw bag window + structured reason for capture + perception-stage timing + output metadata.

In short: rosbag2 snapshot tells us what raw ROS messages were around the event. YERP / EvidenceFlow tells us why this event was worth capturing.

Questions for the ROS community

I would really appreciate feedback from people who debug ROS / ROS2 robots in the field.

Does this EvidenceFlow schema cover the information you would want when debugging ROS perception latency?
For ROS2, should such an adapter start at the image topic level, diagnostics level, or executor / callback timing level?
For AMR / mobile robot scenarios, which fields matter most? (frame latency, message age, queue delay, callback delay, TF wait time, etc.)
How do you currently trigger rosbag2 snapshots in real robots? (Manual trigger? Diagnostics threshold? Topic frequency monitoring? Custom anomaly detector? Lifecycle event? Nav2 state?)
Would a small structured JSON sidecar make snapshot bags easier to triage later?

Boundary

To avoid misunderstanding:

YERP is not a replacement for rosbag2.
YERP is not a replacement for tracing or profilers.
YERP does not identify the final root cause by itself.
YERP tries to capture the input frame and runtime metadata that make a case worth investigating.

My current goal is to turn field debugging from “record everything and search later” into “capture structured evidence when runtime pressure actually happens”.

I am sharing this as an early prototype and schema draft. Comments, criticism, field stories, and suggestions are very welcome!

If you find this “Runtime Evidence Layer” concept valuable for your edge AI ROS deployments, or if you are interested in co-designing the ROS2 adapter together, feel free to drop a comment below, open an issue on GitHub, or reach out to me directly!

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by zc_Liu on July 27, 2026 12:44 PM

[Release] Gazebo ROS Text-to-Speech (TTS) Plugin – From Gazebo Classic to Gazebo Harmonic

from ROS Discourse General

Hello ROS & Gazebo Community,

Weâ€™re excited to announce the release of gz_ros_tts, an open-source ROS 2 Text-to-Speech (TTS) Plugin for the latest Gazebo Harmonic.

This release is the Gazebo Classic and Gazebo harmonic ROS TTS Plugin, bringing the same idea to the modern Gazebo ecosystem with ROS 2.

# Project Evolution

## Version 1 â€” Gazebo Classic ROS TTS Plugin

The first version of the plugin introduced Text-to-Speech support for robots running in Gazebo Classic through ROS.

### Supported

- Gazebo Classic

- ROS 2 Humble

GitHub:

-â€“

## Version 2 â€” gz_ros_tts for Gazebo Harmonic

To support the modern Gazebo ecosystem, the plugin has been redesigned and released as **gz_ros_tts** for Gazebo Harmonic.

### Supported

- Gazebo Harmonic

- ROS 2 Humble

- ROS 2 Jazzy

### Successfully Tested On

- Heinz H1 Humanoid Robot

- Gazebo Harmonic

- ROS 2 Humble

- ROS 2 Jazzy

GitHub:

LinkedIn Release:

Demo Video: classic

Gazebo audio plugin demonstration

Demo video - harmonic

Announcements of gazebo text to speech plugin in gazebo harmonic version

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by Rahul1 on July 26, 2026 07:46 PM

What should go in the tag?

from ROS Discourse General

I’ve always put just “BSD” in the package.xml of our projects, mostly because it is the exact value mentioned in https://www.ros.org/reps/rep-0149.html#license-multiple-but-at-least-one . However, this value is not SPDX.

What’s the current best practice? Should we start putting SPDX identifiers into the license tag? Would it deserve a small update of REP 149?

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by peci1 on July 25, 2026 09:37 AM

A New Chapter for ROS-Industrial Americas

from ROS Industrial

Over the past few months, I've had the privilege of stepping into the role of leading ROS-Industrial Americas. I'm grateful to Matt Robinson for his years of leadership and the strong community he helped build. At our 2026 Annual Meeting, I had the opportunity to share my first impressions and my vision for where we can go next.

I've reproduced that welcome letter below, because it captures the direction I hope we'll pursue together.

This is my first annual meeting as program director for ROS-Industrial Americas, and one thing has become clear very quickly: the strength of ROS-Industrial has never been a single individual or organization. It's the community itself.

Since we announced the leadership transition, I've heard from dozens of members offering encouragement, advice, questions, and ideas.

You made it immediately obvious that you participate in ROS-Industrial not because of a logo or affiliation, but because you genuinely care about advancing industrial robotics. That commitment matters especially now.

It feels like a lot has changed since our last meeting a year ago. Large language models have rapidly evolved from impressive demonstrations into practical tools used daily by engineers around the world. Those same advances are now beginning to reshape robotics as well.

Major technological shifts create uncertainty, but they also create opportunity. One of the reasons ROS-Industrial exists is to help practitioners evaluate emerging technologies, separate signal from noise, and move useful ideas from research into real-world deployment.

For fourteen years now, ROS-I has helped industrial robotics practitioners advance, adopt, and apply technologies that were once difficult to deploy reliably in industrial contexts. Today, we're seeing another transition. Robotic foundation models, LLMs, Physical AI, synthetic data generation, and learning-enabled systems are creating capabilities that seemed impractical not many years ago.

The question for us is no longer whether these technologies will influence industrial robotics. As we will hear from several speakers, they already have. The question is how we incorporate them responsibly, effectively, openly, and practically into real systems.

As I step into this role, I see part of my responsibility as helping this community ask those questions clearly and answer them together.

ROS-Industrial's mission remains the same: enabling innovation in industrial robotics through collaboration, open-source technology, applied research, and education. New technologies will continue to emerge, and our responsibility is to understand them, evaluate them, and make them useful to industrial practitioners. Today, that includes AI. Tomorrow, it will include technologies not yet imagined.

I am grateful to inherit that mission, and I am committed to stewarding it with diligence, openness, and practical focus.

At this year's meeting, our speakers will introduce new open-source robotics software, explore novel applications of AI, discuss new opportunities for open-source automation, and share real-world experience deploying these solutions in specific contexts. Our workshops will give you a focused opportunity to direct the consortium over the coming years.

But the future of ROS-Industrial will not be determined by keynote speakers, steering committees, or roadmap documents alone. It will be shaped by practitioners who bring real problems, real deployments, real successes, and even real failures back to the community.

I offer my sincere thanks to every one of you and your colleagues who have fed this community over the last year: contributing code, sharing lessons learned, participating in projects, mentoring newcomers, and helping move open-source robotics technology from research into production.

I'm excited about this meeting, but I'm more excited about what we'll all do together in the year ahead.

Sincerely,
Jerry Towler
Program Director, ROS-Industrial Americas

by Jerry Towler on July 24, 2026 05:34 PM

Related links

Why this project exists

Current Status and Workflow

Work remaining

I would value your feedback

The first problem: available assets are not automatically usable assets

Why the Robotiq 2F-85 is a useful test case

What we tried from the ROS ecosystem

The remaining fidelity gap

The broader lesson: conversion is not fidelity

Practical takeaways for robotics teams

Toward a better ROS-to-SimReady workflow

Conclusion

What does protoros2 do?

Key Features & Use Cases

Enterprise Security Built-in

Acknowledgement

PLEASE REGISER! brh.eventbrite.com

Background

How to use

Discussion & Future Work

Background

What works and what breaks

The remaining gap

Proposed changes

Relation to existing work

Open questions

What it does

Measured numbers (C++ core, single thread, low-end 2-core CPU)

Where this fits in a ROS stack

Honest limitations

Links

The core idea

Current prototype status

EvidenceFlow Schema v0.1 Draft

Runtime Overhead

How I imagine the rosbag2 integration

Questions for the ROS community

Boundary