Hi, guys
I developing diagnostic programs around whether the command stream, feedback stream, timing window, and physical responses in ROS remain consistent, with a lightweight experimental software package named ros2_kinematic_guard . I have identified recurring issues in the vision‑guided assembly system:
The robot “sees correctly” — but the executed grasp/pose slowly diverges from the expected state over time.
This seems especially common in setups involving:
- RealSense D435i
- TF-based grasp pipelines
- MoveIt servoing
- RGB-D pose estimation
- asynchronous ROS2 nodes
Typical symptoms observed:
- hand-eye calibration gradually becoming inconsistent after thermal drift
- grasp points oscillating despite stable detections
- TF trees remaining valid while pose execution becomes unstable
- frame timestamp mismatch causing “see correctly, grasp incorrectly”
- retry/relocalization logic amplifying small pose residuals
Interestingly, most systems still “look healthy” from standard monitoring:
- bbox/confidence remain high
- TF graph exists
- topics publish normally
- planners succeed
…but the physical execution path drifts.
Therefore, I intend to adopt a lightweight residual monitoring method from ros2_kinematic_guard , which focuses on three indicators:
1. Pose Residual Drift
Monitoring divergence between:
expected_pose(t)
vs
executed_pose(t)
over time.
Especially useful for detecting thermal/mechanical calibration drift.
2. Temporal Coherence Residual
Tracking timestamp alignment between:
- image frame
- TF transform
- depth frame
- grasp pose generation
to detect async ordering issues.
3. Action Stability Residual
Detecting oscillation/jitter in generated grasp points or servo actions across adjacent frames.
This catches cases where the vision system is technically “working” but unstable under lighting/reflection disturbances.
The key idea:
Instead of asking:
“Did perception succeed?”
we ask:
“Did the system state remain converged throughout the perception → planning → action path?”
Curious if others in production robotics are already monitoring these kinds of residuals.
Especially interested in:
- vision-guided assembly
- dynamic calibration compensation
- ROS2 observability
- VLA/VLM action stability
- production deployment diagnostics