I agree with some previous comments, there’s value behind providing a common interface for using ML models in ROS 2 computational graphs. However, it’s to me still very unclear why this group is needed and/or how exactly do you plan do it. In my view, the right way to think about this is to align with ROS 2 standard messages so that practitioners can consider replacing ROS 2 packages/functionality with AI-powered ones (and this is going to take a while 
, if happens).
You mentioned above a few ML libraries and frameworks, let me call these level 1. Myself and my teams in the past contributed gym_gazebo (paper), gym_gazebo2 (paper) and ros2_learn (paper), which many have extended. These and similar projects aim to provide an abstraction layer on top of level 1, so that integrating things into ROS becomes easier for dev. purposes. I’ll call these level 2. Finally, we have projects like openrobotics_darknet_ros, or isaac_ros_pose_estimation which relates to @ggrigor’s input above. These are projects that use AI and produce usefulness that can then be fed into ROS 2 computational graphs. Let’s call these level 3. So what’s your target? It sounds to me like you’re targeting level 1 and if so, then I believe this is not the right forum.
Which ROS 2 AI projects specifically are you looking to align providing a “common interface for accessing and exploiting ML models” @rsanchez? Shouldn’t you start by adressing this first and identify ROS 2 packages that add already value, so that you can work on the interfacing? Also, can you address the questions posted by others above? There’re some really good questions raised.
There’s indeed an overlap in here. Not just with HAWG, but also with Edge AI led by @kydos. Here’s my read:
| WG | Goal |
|---|---|
Hardware Acceleration (HAWG) |
drive creation, maintenance and testing of acceleration kernels on top of open standards (OpenCL) for optimized ROS 2 and Gazebo interactions over different compute substrates (FPGAs, GPUs and ASICs). |
Edge AI |
make Edge AI easier and ubiquitous in ROS 2, specifically ML applied to navigation, perception & picking, inspection and motion planning. |
| AI Integration | provide a common interface for accessing and exploiting ML models. |
For what concerns the HAWG overlap:
- Study implementations of hardware acceleration technologies for ML algorithms.
I think it should be fine provided this group complies with REP-2008 PR and/or helps refine it if needed, so that any contribution is directly useable by the rest of the community looking at hardware acceleration and we don’t create unnecessary fragmentation.