diff --git a/csaxs_bec/bec_ipython_client/plugins/omny/AI_docs/GRIPPER_LOCALIZATION_PLAN.md b/csaxs_bec/bec_ipython_client/plugins/omny/AI_docs/GRIPPER_LOCALIZATION_PLAN.md
index 03bc455f..67585c16 100644
--- a/csaxs_bec/bec_ipython_client/plugins/omny/AI_docs/GRIPPER_LOCALIZATION_PLAN.md
+++ b/csaxs_bec/bec_ipython_client/plugins/omny/AI_docs/GRIPPER_LOCALIZATION_PLAN.md
@@ -103,6 +103,61 @@ implementation effort, consistent with `vision_toolkit.py`'s existing endstation
 (its own module docstring already frames it as shared by "any device or client-side code," OMNY and
 a possible future x-ray-eye-alignment consumer alike).
 
+## Update 2026-09-08: real annotated ground truth, and brightness segmentation's own failure mode
+
+Mirko provided a real photo (closed gripper, pin loaded, only the pin's upper collar visible above
+the receiving piece) with the true parts hand-traced: a left-jaw boundary (red), a right-jaw
+boundary (orange), and the pin/collar (green) -- saved as
+`reference_images/Screenshot 2026-09-08 at 09.42.44.png` (the photo) and
+`reference_images/Slide1.png` (the annotated version, same frame scaled down ~3.73x with white
+padding). This is real ground truth, not a guessed template -- worth using directly rather than
+auto-deriving a template from noisy data again (recommendation 2 above, now actually done).
+
+**Extracted templates from the hand-drawn lines** (color-threshold each line, dilate ~9px into a
+ribbon so `cv2.findContours` gets a closed shape, map Slide1's coordinates back to the full-res
+photo via the measured scale/offset): `left_jaw` (area 31353, centroid (752,186)), `right_jaw` (area
+31334, centroid (1076,186) -- near mirror-symmetric with left_jaw, as expected), `pin` (area 16874,
+centroid (897,405), below and between both jaws). These numbers are real and reusable -- worth
+keeping as an actual template file next time, not re-deriving them again from the PNGs.
+
+**Recommendation 1 (brightness/Otsu segmentation) was tried against these real templates and also
+doesn't cleanly work, for a specific, now-understood reason.** Canny at every threshold/blur
+combination tried only ever found contours up to ~5500px² in this frame -- an order of magnitude
+below the ~17-31k template areas -- confirming (not just suspecting) that Canny never traces the
+gripper's actual outline at all here, at any tuning. Otsu brightness thresholding does find the
+jaws as bright regions, and visually looks promising (dumped to `/tmp/roi_binary2.png` during this
+session, not committed) -- **the two jaw wedges and the small dark notch between them are sharply,
+correctly separated from each other** in the binary image. But a single connected blob of ~150-217k
+px² still results, because **the pin's own backlit glow forms a bright vertical column that bridges
+the base of both jaws together**, and the top of both jaws also fades into equally-bright
+out-of-focus background with no boundary between them. Excluding near-saturated (>250) pixels as a
+separate "glare" class didn't break this bridge either -- the connecting glow isn't uniformly
+saturated to 255 throughout.
+
+**So the real obstacle isn't "edges vs. brightness" as a technique choice -- it's that the pin's own
+illumination (presumably deliberate backlighting for a different purpose) visually connects
+everything bright in the frame into one blob, defeating both approaches' assumption that the
+gripper is a boundary-enclosed region.** Next things worth trying, in rough order of effort:
+
+1. **Work in color, not grayscale, before segmenting.** `to_grayscale()` was applied immediately in
+   every attempt above -- the pin's glow may have a different color temperature (warmer/more
+   saturated yellow, per the raw photo) than the gripper's own metal reflection, in which case a
+   color-based mask (e.g. HSV saturation/hue, not just intensity) might separate "light source
+   glow" from "reflective metal surface" where plain brightness cannot.
+2. **Explicitly model and mask out the glow column.** Its rough position (a vertical band directly
+   below the notch, between the jaws) is predictable across captures -- subtracting or excluding a
+   known glow-shaped region before segmenting might be enough to let the jaws separate on their own.
+3. **Geometric/line-based detection instead of blob-based.** The Otsu binary shows each jaw's visible
+   boundary is fundamentally two near-straight converging edges (a wedge/V shape) -- `cv2.HoughLinesP`
+   to find line segments, then searching for a *pair* of lines whose angle/convergence matches the
+   known gripper geometry, sidesteps the "closed blob" assumption entirely (this also matches what
+   Mirko's own hand-drawn annotation actually is -- open boundary curves, not closed part outlines;
+   the annotator only traced what was genuinely visible as a clean edge, same constraint the
+   algorithm is hitting).
+4. Steps 2-5 from the original recommendation list (deliberate template curation, gripper-relative
+   pin ROI, feature matching, post-localization reference diffing) are unaffected by this update and
+   still apply once *some* localization technique above actually works.
+
 ## What this doesn't touch
 
 `VisionInterface`'s edge-threshold/blur tuning (this session's other change) and the `VisionTuning`