I’m working on a project in the robotics space and would love to get the community’s perspective.
The problem I’ve seen: robotics teams generate a massive amount of data (ROS2, MCAP, OpenLABEL, etc.), but debugging and analysis often means hours of digging through logs, building custom scripts, or fighting fragmented formats. For small and medium robotics companies, this can really slow down iteration.
I’m trying to understand:
• How do you/your team currently manage and analyze robot data?
• What are the biggest pain points you face (e.g. debugging failures, comparing test runs, searching across logs)?
• Have you tried tools like Foxglove/Rerun/etc.? What works, what doesn’t?
• If there was a solution that actually made this easier, what would it have to do for you to adopt it?
I also put together a short (5 min) survey here: https://forms.gle/x57UReg8Yj9Gx7qZ8
If you’re willing to share your experiences in more detail, it would really help shape what we’re building.
We’ll anonymize responses and share the aggregated insights back with the community once we’ve collected enough.
Thanks in advance — I know this is a niche problem, but I figured HN has some of the sharpest robotics engineers, founders, and tinkerers out there. Really curious to hear how you’re solving this today.
* Combing through the syslogs to find issues is an absolute nightmare, even more so if you are told that the machine broke at some point last night
* Even if you find the error, it's not necessarily when something broke; it could have happened way before, but you just discovered it because the system hit a state that required it
* If combing through syslog is hard, try rummaging through multiple mcap files by hand to see where a fault happened
* The hardware failing silently is a big PITA - this is especially true for things that read analog signals (think PLCs)
Many of the above issues can be solved with the right architecture or tooling, but often the teams I joined didn't have it, and lacked the capacity to develop it.
At Foxglove, we make it easy to aggregate and visualize the data and have some helper features (e.g., events, data loaders) that can speed up workflows. However, I would say that having good architecture, procedures, and an aligned team goes a long way in smoothing out troubleshooting, regardless of the tools.