Good question, a bit of both, honestly. We’re exploring whether AI can meaningfully reduce the time teams spend diagnosing complex robotics failures. A lot of current tooling stops at visualization, and we’re curious if there’s room for an intelligence layer that helps correlate and triage data automatically. So I’m mostly here to stress test assumptions and learn how practitioners actually work.
True, for isolated signals, absolutely. But in real-world robotics systems, the challenge isn’t doing the math, it’s seeing the context.
Timing drift or sensor desync rarely appear as clean numerical mismatches, they emerge across hundreds of async topics, network delays, or subtle hardware degradations. Arithmetic can flag the symptom, but not always the cause or pattern that leads to it.
The idea behind AI here isn’t to replace deterministic checks, it’s to augment them. Think of it as spotting correlations or early warning trends that static rules can’t (like cross-sensor covariance shifts before failure).
Arithmetic finds the what; AI helps predict the why and when.
Thanks a lot for sharing this resource! I wasn’t aware of the Cloud Robotics Working Group, those sessions look super relevant. I’ll definitely check out the recordings and join future meetings. Our angle is very aligned: we’re exploring how AI/automation can help with the time sink of debugging large-scale ROS/ROS 2 systems, especially when logs/bag files pile up. It’d be valuable to hear what the community feels is still missing, even with the current set of tools. Do you think there’s space for a layer focused purely on automated error detection and root cause suggestions?
"automated error detection" -- how do you want to do that? How would you define "error". Clearly you are not just proposing to detect "error" lines in the log, because that's trivial. But if you don't, then how would you define and detect errors and auto-root-cause them? Maybe we can discuss at one of the next meetings.
errors are rarely explicit in robots, they're often emergent from complex interactions, like a silent drift in AMCL localization causing a downstream collision, or sporadic packet loss in DDS causing desynchronized multirobot coordination. We'd describe errors dynamically through a mix of domain rules, unsupervised ML, and generative AI;
* Start with user-determined or auto-deduced invariants from "nominal" runs (e.g., "joint torque variance should never exceed 10% during unloaded motion," derived from historical MCAP bags). This takes inspiration from model-based verification techniques in current ROS2 research, e.g., automated formal verification with model driven engineering.
* use light, edge-optimized models (e.g., graph neural networks or variational autoencoders) to monitor ROS topic multivariate time series (/odom, /imu, /camera/image_raw). Fuse visual and sensor input using multimodal LLMs (fine-tuned on e.g. nuScenes or custom robot logs) to detect "silent failures" e.g., detect a LiDAR occlusion not reflected in logs but apparent from point cloud entropy spikes cross-checked against camera frames.
* Utilize GenAI (e.g., versions of GPT-4o or Llama) for NLP on logs, classifying ambiguous events like "nav stack increased latency" as a predictor for failure. This predictive approach is an improvement of the ROS Help Desk's GenAI model that already demonstrates 70-80% decrease in debugging time by indicating issues before full failure.
This is not hypothesizing; there are already PyTorch and ROS2 plugin prototype versions with ~90% accuracy detection in Gazebo simulation failures, and dynamic covariance compensation (as used in more recent AI-facilitated ROS2 localization studies) takes care of noisy real-world data.
The detection pipeline that is automatic will be akin to where the system receives live streams or bag files via a ROS2-compatible middleware (e.g., based on more recent flexible integration layers for task orchestration), then processes in streaming fashion then:
* map heterogeneous formats (MCAP, OpenLABEL, JSON logs) to a temporal knowledge graph
nodes for components (sensors, planners), edges for causal dependencies and timestamps.
Enables holistic analysis, as opposed to fragmented tools.
* Route the data through Apache Flink or Kafka combined ML pipelines for windowed detection. For instance, flag a "error" if a robot's velocity profile is beyond predicted physics models (with Control or PySDF libraries), even without explicit logs—combining environmental context from combined BIM/APS for vision use.
* subsequently employ uncertainty sampling through large language models to solicit user input on borderline scenarios, progressively fine‑tuning the models. Benchmark outcomes from SYSDIAGBENCH indicate that LLMs such as GPT‑4 perform exceptionally well, correctly identifying robotic problems in 85 % of cases across various model scales.
I trust this provides some insight; we are currently testing a prototype that fuses these components into a real‑time observability framework for ROS2. Although still in its infancy, it already demonstrates encouraging accuracy on both simulated and real‑world data sets. I would appreciate your thoughts on sharpening the notion of “error” for multi‑agent or hybrid systems, particularly in contexts where emergent behavior makes it hard to distinguish between anomalies and adaptive responses.
Thanks for sharing! You've clearly done your homework. Can you contact me, e.g., on LinkedIn? I'd love to explore with you whether what you want to build could benefit from the open-source framework we've built for developing new full-stack robotic capabilities (Transitive, https://transitiverobotics.com/docs/learn/intro/).
Really appreciate the offer, we’d love to take you up on it. A lot of what we’re exploring right now comes down to signal analysis and anomaly detection in robotics data, which gets math-heavy fast (especially when combining time-series data from multiple sources). We’re setting up short user interviews with roboticists/devs to better map the pain points. Would you be open to a quick chat about the trickiest math/log parsing issues you’ve faced? It could help us avoid reinventing the wheel.
This is super insightful, thank you for laying it out so clearly. Your point about the error surfacing way after it first occurred is exactly the sort of issue we’re interested in tackling. Foxglove is doing a great job with visualization and aggregation; what we’re thinking is more of a complementary diagnostic layer that:
• Correlates syslogs with mcap/bag file anomalies automatically
• Flags when a hardware failure might have begun (not just when it manifests)
• Surfaces probable root causes instead of leaving teams to manually chase timestamps
From your experience across 50+ clients, which do you think is the bigger timesink: data triage across multiple logs/files or interpreting what the signals actually mean once you’ve found them?
Our current thinking is to focus heavily on automating triage across syslogs and bag/mcap files, since that’s where the hours really get burned, even for experienced folks. For interpretation, we see it more as an assistive layer (e.g., surfacing “likely causes” or linking to past incidents), rather than trying to replace domain expertise.
Do you think there are specific triage workflows where even a small automation (say, correlating error timestamps across syslog and bag files) would save meaningful time?
One thing that comes to mind is checking the timestamps across sensors and other topics. Two cases come to mind:
* I was setting up Ouster lidar to use gos time, don’t remember the details now but it was reporting the time ~32 seconds in the past (probably some leap seconds setting?)
* I had a ROS node misbehaving in some weird ways - it turned out there was a service call to insert something into db and for some reason the db started taking 5+ minutes to complete which wasn’t really appropriate for a blocking call
I think the timing is one thing that needs to be consistently done right on every platform. The other issues I came across were very application specific.
