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+# Work-plane calibration + below-plane filter — Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Calibrate the work-plane from a user-drawn rectangle on the RGB image, persist it with a configurable `offset_mm`, and use it to drop points below the plane in every object-cloud-producing endpoint (`/scan`, `/object_points`, `/grasp/plan`, `/grasp/execute`). Repurpose the existing "Servo Visuale" tab as "Calibrazione Piano".
+
+**Architecture:** A single new module `perception/work_plane.py` owns the `WorkPlane` dataclass (extended with `offset_mm`), `fit_plane_svd`, the disk-backed `get/set_work_plane` singleton, and the new `apply_below_plane_filter` helper. Four new endpoints under `/api/work_plane/*` handle calibration (preview/save/get/delete). The filter is called at three call sites (sam21_bridge, `_points_via_bbox`, `_object_cloud_cv` bbox branch). The visual-servo feature is removed entirely; the GUI tab is rewritten.
+
+**Tech Stack:** Python 3.10 · FastAPI · `httpx` · `numpy` · `opencv-contrib-python-headless` · `pytest` (existing) · vanilla JS + Three.js (existing in `servo.js`'s viewer code, will be reused in `work_plane.js`).
+
+**Design doc:** `docs/superpowers/specs/2026-05-11-work-plane-filter-design.md`.
+
+**Critical engineering facts:**
+- `WorkPlane.normal` is in CV/OpenCV camera frame and oriented toward the camera (negative Z component in OpenCV convention). Points "above the plane" (where objects sit) have positive `signed_distance = normal · X + offset`.
+- `nuwa.pointcloud_roi` returns points in Nuwa-native frame `[x, -y, -z]` despite its current docstring; the existing `/plane/fit` endpoint applies `nuwa_to_cv_frame` (which does the `(y,z) → (-y,-z)` flip again) before fitting. The new `/api/work_plane/fit` keeps this exact behaviour for continuity with calibrations recorded under the current pipeline.
+- The bridge helper `points_via_sam21` already returns points in CV frame (SAM21's native output), so it does NOT need `nuwa_to_cv_frame`.
+- The bbox branch of `_object_cloud_cv` and `_points_via_bbox` both call `nuwa_to_cv_frame` on the ROI cloud — the filter goes AFTER that flip.
+- Filter math: keep `signed_dist >= -offset_m`, where `offset_m = offset_mm / 1000`. Positive `offset_mm` is a permissive buffer (preserves points up to `offset_mm` below the plane).
+
+---
+
+## File Structure
+
+| File | Action | Responsibility |
+|---|---|---|
+| `src/presa_mano/perception/work_plane.py` | create | `WorkPlane` dataclass (with `offset_mm`), `fit_plane_svd`, `set/get_work_plane`, disk persistence, `apply_below_plane_filter` helper, `WorkPlaneError` exception. |
+| `tests/test_work_plane.py` | create | Unit tests for fit, filter, persistence (including legacy-file load). |
+| `src/presa_mano/perception/sam21_bridge.py` | modify | Call `apply_below_plane_filter` on `pts_cv` after SAM21 returns it; raise `Sam21Fallback` if filtered cloud < 10 points. |
+| `src/presa_mano/api/grasp_routes.py` | modify | In `_points_via_bbox`: apply filter after `nuwa_to_cv_frame`. Surface `plane_applied` + `n_points_before_plane` in meta. **Later (Task 5):** remove all visual-servo endpoints, models, helpers, imports. |
+| `src/presa_mano/api/perception_routes.py` | modify | In `_object_cloud_cv` bbox branch: apply filter after `nuwa_to_cv_frame`. Surface `plane_applied` + `n_points_before_plane` in `/scan` and `/object_points` responses. |
+| `src/presa_mano/api/work_plane_routes.py` | create | Router with `/fit`, `/save`, `GET`, `DELETE` endpoints. |
+| `src/presa_mano/main.py` | modify | Register `work_plane_routes` router under `/api/work_plane`. Update cache-bust list (`servo.js` → `work_plane.js`). |
+| `src/presa_mano/grasp/visual_servo.py` | delete | Removed in Task 5; the plane bits already migrated to `perception/work_plane.py` in Task 1. |
+| `web/index.html` | modify | Rename tab "Servo Visuale" → "Calibrazione Piano". Replace `#tab-servo` panel content with the new layout. |
+| `web/servo.js` | delete | Replaced by `web/work_plane.js`. |
+| `web/work_plane.js` | create | ROI drawing on RGB, fit-preview call, 3D rendering of plane + colored scatter, offset input, save/clear actions. |
+
+---
+
+## Task 1: `perception/work_plane.py` module (TDD)
+
+Move and extend the plane code into a focused module, then write the new `apply_below_plane_filter` helper.
+
+**Files:**
+- Create: `src/presa_mano/perception/work_plane.py`
+- Create: `tests/test_work_plane.py`
+
+- [ ] **Step 1: Write the failing tests**
+
+Create `tests/test_work_plane.py`:
+
+```python
+"""Unit tests for perception.work_plane."""
+from __future__ import annotations
+
+import json
+import tempfile
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from presa_mano.perception.work_plane import (
+    WorkPlane,
+    WorkPlaneError,
+    apply_below_plane_filter,
+    fit_plane_svd,
+    _load_work_plane_from_disk,
+    _save_work_plane_to_disk,
+    _work_plane_path,
+)
+
+
+def _flat_plane_cloud(z: float = 1.0, n: int = 200, noise_m: float = 0.0):
+    """Cloud sampled on the plane z=z (camera frame, OpenCV: camera looks +Z)."""
+    rng = np.random.default_rng(0)
+    xy = rng.uniform(-0.2, 0.2, size=(n, 2))
+    zs = np.full((n, 1), z) + rng.normal(0, noise_m, size=(n, 1))
+    return np.hstack([xy, zs])
+
+
+def test_fit_plane_svd_horizontal_plane():
+    """Plane at z=1.0, normal must point toward camera (negative Z)."""
+    pts = _flat_plane_cloud(z=1.0, n=200, noise_m=0.0)
+    normal, offset, centroid, rms_mm = fit_plane_svd(pts)
+    assert abs(normal[2]) > 0.99
+    assert normal[2] < 0
+    assert abs(offset - 1.0) < 1e-3
+    assert rms_mm < 1.0
+
+
+def test_fit_plane_svd_raises_on_too_few_points():
+    with pytest.raises(WorkPlaneError):
+        fit_plane_svd(np.zeros((5, 3)))
+
+
+def test_apply_below_plane_filter_noop_without_plane():
+    pts = _flat_plane_cloud(z=1.0)
+    out, info = apply_below_plane_filter(pts, None)
+    assert info["plane_applied"] is False
+    assert info["n_points_before_plane"] == len(pts)
+    assert out is pts
+
+
+def test_apply_below_plane_filter_keeps_above_drops_below():
+    """Plane at z=1.0, normal toward camera (-Z): points with z<1.0 are above."""
+    above = _flat_plane_cloud(z=0.95, n=100)
+    below = _flat_plane_cloud(z=1.10, n=100)
+    pts = np.vstack([above, below])
+    wp = WorkPlane(
+        normal=[0.0, 0.0, -1.0], offset=1.0,
+        centroid=[0.0, 0.0, 1.0],
+        n_inliers=100, fit_rms_mm=0.0,
+        roi=[0, 0, 10, 10],
+        offset_mm=0.0,
+        created_at=0.0,
+    )
+    out, info = apply_below_plane_filter(pts, wp)
+    assert info["plane_applied"] is True
+    assert info["n_points_before_plane"] == 200
+    assert len(out) == 100
+    assert np.all(out[:, 2] <= 1.0 + 1e-9)
+
+
+def test_apply_below_plane_filter_offset_buffer_keeps_below():
+    """offset_mm=50 keeps points up to 50mm below the plane."""
+    above = _flat_plane_cloud(z=0.95, n=50)
+    just_below = _flat_plane_cloud(z=1.03, n=50)
+    far_below = _flat_plane_cloud(z=1.20, n=50)
+    pts = np.vstack([above, just_below, far_below])
+    wp = WorkPlane(
+        normal=[0.0, 0.0, -1.0], offset=1.0,
+        centroid=[0.0, 0.0, 1.0],
+        n_inliers=50, fit_rms_mm=0.0,
+        roi=[0, 0, 10, 10],
+        offset_mm=50.0,
+        created_at=0.0,
+    )
+    out, info = apply_below_plane_filter(pts, wp)
+    assert info["plane_applied"] is True
+    assert len(out) == 100   # above (50) + just_below (50); far_below dropped
+
+
+def test_apply_below_plane_filter_empty_input():
+    wp = WorkPlane(
+        normal=[0.0, 0.0, -1.0], offset=1.0,
+        centroid=[0.0, 0.0, 1.0],
+        n_inliers=10, fit_rms_mm=0.0,
+        roi=[0, 0, 10, 10],
+        offset_mm=5.0, created_at=0.0,
+    )
+    out, info = apply_below_plane_filter(np.empty((0, 3)), wp)
+    assert info["plane_applied"] is False   # short-circuit: empty input never "ran"
+    assert info["n_points_before_plane"] == 0
+    assert len(out) == 0
+
+
+def test_work_plane_disk_roundtrip(tmp_path, monkeypatch):
+    """Save then load preserves all fields including offset_mm."""
+    monkeypatch.setenv("PRESA_MANO_DATA", str(tmp_path))
+    wp = WorkPlane(
+        normal=[0.0, 0.0, -1.0], offset=1.0,
+        centroid=[0.1, 0.2, 1.0],
+        n_inliers=42, fit_rms_mm=1.5,
+        roi=[10, 20, 100, 200],
+        offset_mm=7.5,
+        created_at=1234567890.0,
+    )
+    _save_work_plane_to_disk(wp)
+    loaded = _load_work_plane_from_disk()
+    assert loaded is not None
+    assert loaded.normal == wp.normal
+    assert loaded.offset == wp.offset
+    assert loaded.centroid == wp.centroid
+    assert loaded.n_inliers == wp.n_inliers
+    assert loaded.fit_rms_mm == wp.fit_rms_mm
+    assert loaded.roi == wp.roi
+    assert loaded.offset_mm == 7.5
+    assert loaded.created_at == wp.created_at
+
+
+def test_work_plane_load_legacy_file_defaults_offset(tmp_path, monkeypatch):
+    """A pre-existing work_plane.json without offset_mm gets the 5.0 default."""
+    monkeypatch.setenv("PRESA_MANO_DATA", str(tmp_path))
+    path = _work_plane_path()
+    legacy = {
+        "normal": [0.0, 0.0, -1.0], "offset": 1.0,
+        "centroid": [0.0, 0.0, 1.0],
+        "n_inliers": 10, "fit_rms_mm": 1.0,
+        "roi": [0, 0, 10, 10],
+        "created_at": 0.0,
+    }
+    path.write_text(json.dumps(legacy))
+    loaded = _load_work_plane_from_disk()
+    assert loaded is not None
+    assert loaded.offset_mm == 5.0
+```
+
+- [ ] **Step 2: Run tests to verify they fail**
+
+Run:
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run pytest tests/test_work_plane.py -v
+```
+
+Expected: all tests FAIL with `ModuleNotFoundError: No module named 'presa_mano.perception.work_plane'`.
+
+- [ ] **Step 3: Implement `perception/work_plane.py`**
+
+Create `src/presa_mano/perception/work_plane.py`:
+
+```python
+"""Work-plane fit, persistence, and below-plane filter.
+
+The work plane is fitted from a user-drawn rectangle on the RGB image. The
+fit uses SVD on the bbox depth cloud (after `nuwa_to_cv_frame`), so the
+plane normal lives in OpenCV RGB-camera frame and is oriented toward the
+camera (negative Z component in OpenCV convention).
+
+The filter `apply_below_plane_filter` removes points more than `offset_mm`
+below the plane: keep `(normal · X + offset) >= -offset_mm/1000`. A
+positive `offset_mm` is permissive (preserves a buffer below the plane).
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+import numpy as np
+
+from ..config import data_dir
+
+log = logging.getLogger(__name__)
+
+
+class WorkPlaneError(RuntimeError):
+    """Bad input to `fit_plane_svd` (too few points, degenerate cloud)."""
+
+
+@dataclass
+class WorkPlane:
+    """Plane in CV camera frame. `normal · X + offset = 0`; `normal` is
+    unit-length and oriented toward the camera (negative Z in OpenCV)."""
+    normal: list[float]
+    offset: float
+    centroid: list[float]
+    n_inliers: int
+    fit_rms_mm: float
+    roi: list[int]
+    offset_mm: float
+    created_at: float
+
+
+_WORK_PLANE: WorkPlane | None = None
+_WORK_PLANE_LOADED = False
+
+
+def _work_plane_path() -> Path:
+    return data_dir() / "work_plane.json"
+
+
+def _save_work_plane_to_disk(wp: WorkPlane | None) -> None:
+    path = _work_plane_path()
+    try:
+        if wp is None:
+            if path.exists():
+                path.unlink()
+            return
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(asdict(wp), indent=2))
+    except Exception as e:
+        log.warning("failed to persist work plane: %s", e)
+
+
+def _load_work_plane_from_disk() -> WorkPlane | None:
+    path = _work_plane_path()
+    try:
+        with open(path, "r") as f:
+            d = json.load(f)
+        d.setdefault("offset_mm", 5.0)   # legacy files
+        return WorkPlane(**d)
+    except FileNotFoundError:
+        return None
+    except Exception as e:
+        log.warning("failed to load persisted work plane (%s)", e)
+        return None
+
+
+def get_work_plane() -> WorkPlane | None:
+    global _WORK_PLANE, _WORK_PLANE_LOADED
+    if not _WORK_PLANE_LOADED:
+        _WORK_PLANE = _load_work_plane_from_disk()
+        _WORK_PLANE_LOADED = True
+    return _WORK_PLANE
+
+
+def set_work_plane(wp: WorkPlane | None) -> None:
+    global _WORK_PLANE, _WORK_PLANE_LOADED
+    _WORK_PLANE = wp
+    _WORK_PLANE_LOADED = True
+    _save_work_plane_to_disk(wp)
+
+
+def fit_plane_svd(points_xyz: np.ndarray
+                  ) -> tuple[np.ndarray, float, np.ndarray, float]:
+    """Least-squares plane via SVD. Returns (normal, offset, centroid, rms_mm).
+
+    Plane equation: `normal · X + offset = 0`. Normal is oriented toward
+    the camera origin (so `signed_distance = normal · X + offset` is
+    positive for points on the camera side of the plane)."""
+    pts = np.asarray(points_xyz, dtype=np.float64)
+    if pts.ndim != 2 or pts.shape[1] < 3:
+        raise WorkPlaneError(f"unexpected points shape for plane fit: {pts.shape}")
+    xyz = pts[:, :3]
+    if len(xyz) < 10:
+        raise WorkPlaneError(f"too few points to fit plane: {len(xyz)}")
+    c = xyz.mean(axis=0)
+    centered = xyz - c
+    _, _, Vt = np.linalg.svd(centered, full_matrices=False)
+    normal = Vt[-1]
+    n = float(np.linalg.norm(normal))
+    if n < 1e-9:
+        raise WorkPlaneError("degenerate plane (zero normal)")
+    normal = normal / n
+    # Orient toward camera origin: signed distance of origin from plane must
+    # be positive, i.e. `-n·c > 0`, i.e. `n·c < 0`.
+    if float(normal @ c) > 0:
+        normal = -normal
+    offset = -float(normal @ c)
+    signed = centered @ normal
+    rms_mm = float(np.sqrt(np.mean(signed * signed)) * 1000.0)
+    return normal, offset, c, rms_mm
+
+
+def apply_below_plane_filter(
+    pts_cv: np.ndarray,
+    wp: WorkPlane | None,
+) -> tuple[np.ndarray, dict]:
+    """Drop points more than `offset_mm` below the plane.
+
+    Returns `(filtered_pts, info)`. `info` always carries:
+        plane_applied (bool): true iff a plane was provided AND there were
+            points to filter
+        n_points_before_plane (int): len(pts_cv) on entry
+    """
+    info = {"plane_applied": False,
+            "n_points_before_plane": int(len(pts_cv))}
+    if wp is None or len(pts_cv) == 0:
+        return pts_cv, info
+    n_vec = np.asarray(wp.normal, dtype=np.float64)
+    d_off = float(wp.offset)
+    offset_m = float(wp.offset_mm) / 1000.0
+    signed = pts_cv @ n_vec + d_off
+    keep = signed >= -offset_m
+    info["plane_applied"] = True
+    return pts_cv[keep], info
+```
+
+- [ ] **Step 4: Run tests to verify they pass**
+
+Run:
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run pytest tests/test_work_plane.py -v
+```
+
+Expected: 8 PASS.
+
+If any test fails, debug before moving on. The `monkeypatch.setenv("PRESA_MANO_DATA", str(tmp_path))` relies on `data_dir()` reading the env var lazily — if `data_dir()` caches at import time, you may need to import `data_dir` inside the helpers (not at module top). Check `src/presa_mano/config.py` for the actual behaviour.
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add tests/test_work_plane.py src/presa_mano/perception/work_plane.py
+git commit -m "feat(perception): work_plane module with below-plane filter
+
+Owns WorkPlane (with new offset_mm field), fit_plane_svd, disk-backed
+get/set, and the apply_below_plane_filter helper. WorkPlane.normal lives
+in OpenCV RGB-camera frame and is oriented toward the camera. The filter
+keeps points where signed_distance >= -offset_mm/1000.
+
+Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Task 2: Wire the filter into the three call sites
+
+Apply `apply_below_plane_filter` in `sam21_bridge.points_via_sam21`, `grasp_routes._points_via_bbox`, and `perception_routes._object_cloud_cv` bbox branch. Surface `plane_applied` + `n_points_before_plane` in API responses.
+
+**Files:**
+- Modify: `src/presa_mano/perception/sam21_bridge.py`
+- Modify: `src/presa_mano/api/grasp_routes.py`
+- Modify: `src/presa_mano/api/perception_routes.py`
+
+- [ ] **Step 1: Add filter to `points_via_sam21` in sam21_bridge.py**
+
+In `src/presa_mano/perception/sam21_bridge.py`, add this import near the top with the other relative imports:
+
+```python
+from .work_plane import apply_below_plane_filter, get_work_plane
+```
+
+Then, after the `pts_cv = sam21.segment_pointcloud(...)` call returns successfully and before the existing `if pts_cv.shape[0] < 10` check, insert:
+
+```python
+    pts_cv, plane_info = apply_below_plane_filter(pts_cv, get_work_plane())
+    if pts_cv.shape[0] < 10:
+        log.warning("SAM21 cloud has %d points after plane filter (<10), falling back to bbox",
+                    pts_cv.shape[0])
+        raise Sam21Fallback(
+            f"too few points after plane filter: {pts_cv.shape[0]}"
+            f" (before plane: {plane_info['n_points_before_plane']})"
+        )
+```
+
+Replace the OLD `<10` check (which compared `pts_cv.shape[0] < 10` against the SAM21-returned cloud) with this new block — the new block does both: applies the filter AND enforces the `<10` minimum.
+
+Note: `points_via_sam21` does not currently return `plane_info`. Update the function signature: change the return type to `tuple[np.ndarray, dict]` and return `(pts_cv, plane_info)` at the end. Update the docstring to describe the new return.
+
+The full final tail of `points_via_sam21` should look like:
+
+```python
+    pts_cv, plane_info = apply_below_plane_filter(pts_cv, get_work_plane())
+    if pts_cv.shape[0] < 10:
+        log.warning("SAM21 cloud has %d points after plane filter (<10), falling back to bbox",
+                    pts_cv.shape[0])
+        raise Sam21Fallback(
+            f"too few points after plane filter: {pts_cv.shape[0]}"
+            f" (before plane: {plane_info['n_points_before_plane']})"
+        )
+    return pts_cv, plane_info
+```
+
+- [ ] **Step 2: Update callers of `points_via_sam21`**
+
+There are TWO callers:
+
+**Caller 1**: `src/presa_mano/api/grasp_routes.py` inside `_object_points_in_base`. Current code:
+
+```python
+            pts_cv = points_via_sam21(nuwa, _deps.sam21(), bbox, sw)
+            with sw.step("to_base_frame"):
+                pts_base = transform_points(T_base_cam, pts_cv)
+            meta["source"] = "sam21"
+            meta["n_points"] = int(len(pts_base))
+            return pts_base, meta
+```
+
+Replace with:
+
+```python
+            pts_cv, plane_info = points_via_sam21(nuwa, _deps.sam21(), bbox, sw)
+            with sw.step("to_base_frame"):
+                pts_base = transform_points(T_base_cam, pts_cv)
+            meta["source"] = "sam21"
+            meta["n_points"] = int(len(pts_base))
+            meta["plane_applied"] = plane_info["plane_applied"]
+            meta["n_points_before_plane"] = plane_info["n_points_before_plane"]
+            return pts_base, meta
+```
+
+**Caller 2**: `src/presa_mano/api/perception_routes.py` inside `_object_cloud_cv`. Current code:
+
+```python
+    if cfg.get("grasp", {}).get("use_sam21", True):
+        try:
+            pts_cv = points_via_sam21(nuwa, _deps.sam21(), bbox, sw)
+            if len(pts_cv) > max_points:
+                idx = np.random.choice(len(pts_cv), max_points, replace=False)
+                pts_cv = pts_cv[idx]
+            return pts_cv, "sam21", extra
+        except Sam21Fallback as e:
+            extra["sam21_fallback_reason"] = str(e)
+```
+
+Replace with:
+
+```python
+    if cfg.get("grasp", {}).get("use_sam21", True):
+        try:
+            pts_cv, plane_info = points_via_sam21(nuwa, _deps.sam21(), bbox, sw)
+            extra["plane_applied"] = plane_info["plane_applied"]
+            extra["n_points_before_plane"] = plane_info["n_points_before_plane"]
+            if len(pts_cv) > max_points:
+                idx = np.random.choice(len(pts_cv), max_points, replace=False)
+                pts_cv = pts_cv[idx]
+            return pts_cv, "sam21", extra
+        except Sam21Fallback as e:
+            extra["sam21_fallback_reason"] = str(e)
+```
+
+- [ ] **Step 3: Add filter to `_points_via_bbox` in grasp_routes.py**
+
+In `src/presa_mano/api/grasp_routes.py`, add this import near the existing imports:
+
+```python
+from ..perception.work_plane import apply_below_plane_filter, get_work_plane
+```
+
+Then, locate `_points_via_bbox`. Current body (around lines 106-130):
+
+```python
+def _points_via_bbox(nuwa, T_base_cam: np.ndarray, bbox: tuple[int, int, int, int],
+                     sw: Stopwatch) -> np.ndarray:
+    """Legacy path: bbox → Nuwa ROI cloud → filter outliers → base frame.
+
+    Raises HTTPException(422) on <10 valid depth points (existing behaviour).
+    """
+    cfg = get_config()
+    u_min, v_min, u_max, v_max = bbox
+    try:
+        with sw.step("pointcloud_roi"):
+            pc, _meta = nuwa.pointcloud_roi(
+                u_min, v_min, u_max, v_max,
+                step=int(cfg.get("grasp", {}).get("pointcloud_step", 1)),
+                max_depth_mm=int(cfg.get("grasp", {}).get("pointcloud_max_depth_mm", 2000)),
+            )
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=f"Nuwa ROI pointcloud failed: {e}")
+    with sw.step("filter_outliers"):
+        cropped = filter_outliers(pc)
+    if len(cropped) < 10:
+        raise HTTPException(status_code=422, detail=f"too few valid depth points in bbox: {len(cropped)}")
+    with sw.step("to_base_frame"):
+        pts_cv = nuwa_to_cv_frame(cropped[:, :3])
+        pts_base = transform_points(T_base_cam, pts_cv)
+    return pts_base
+```
+
+Change the return type to `tuple[np.ndarray, dict]` and replace the body with:
+
+```python
+def _points_via_bbox(nuwa, T_base_cam: np.ndarray, bbox: tuple[int, int, int, int],
+                     sw: Stopwatch) -> tuple[np.ndarray, dict]:
+    """Legacy path: bbox → Nuwa ROI cloud → filter outliers → plane filter → base frame.
+
+    Raises HTTPException(422) on <10 valid depth points (after both
+    outlier filter and below-plane filter).
+    """
+    cfg = get_config()
+    u_min, v_min, u_max, v_max = bbox
+    try:
+        with sw.step("pointcloud_roi"):
+            pc, _meta = nuwa.pointcloud_roi(
+                u_min, v_min, u_max, v_max,
+                step=int(cfg.get("grasp", {}).get("pointcloud_step", 1)),
+                max_depth_mm=int(cfg.get("grasp", {}).get("pointcloud_max_depth_mm", 2000)),
+            )
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=f"Nuwa ROI pointcloud failed: {e}")
+    with sw.step("filter_outliers"):
+        cropped = filter_outliers(pc)
+    pts_cv = nuwa_to_cv_frame(cropped[:, :3])
+    pts_cv, plane_info = apply_below_plane_filter(pts_cv, get_work_plane())
+    if len(pts_cv) < 10:
+        raise HTTPException(
+            status_code=422,
+            detail=f"too few valid depth points in bbox after plane filter: {len(pts_cv)}"
+                   f" (before plane: {plane_info['n_points_before_plane']})"
+        )
+    with sw.step("to_base_frame"):
+        pts_base = transform_points(T_base_cam, pts_cv)
+    return pts_base, plane_info
+```
+
+- [ ] **Step 4: Update the caller of `_points_via_bbox` in `_object_points_in_base`**
+
+Inside `_object_points_in_base`, change the fall-through path to receive the plane_info tuple. Current:
+
+```python
+    pts_base = _points_via_bbox(nuwa, T_base_cam, bbox, sw)
+    meta["source"] = "bbox_roi"
+    meta["n_points"] = int(len(pts_base))
+    return pts_base, meta
+```
+
+Replace with:
+
+```python
+    pts_base, plane_info = _points_via_bbox(nuwa, T_base_cam, bbox, sw)
+    meta["source"] = "bbox_roi"
+    meta["n_points"] = int(len(pts_base))
+    meta["plane_applied"] = plane_info["plane_applied"]
+    meta["n_points_before_plane"] = plane_info["n_points_before_plane"]
+    return pts_base, meta
+```
+
+- [ ] **Step 5: Add filter to `_object_cloud_cv` bbox branch in perception_routes.py**
+
+In `src/presa_mano/api/perception_routes.py`, add the import near the existing imports:
+
+```python
+from ..perception.work_plane import apply_below_plane_filter, get_work_plane
+```
+
+Locate the bbox branch of `_object_cloud_cv`. Current code (after the SAM21 branch):
+
+```python
+    u_min, v_min, u_max, v_max = bbox
+    step = int(cfg.get("grasp", {}).get("pointcloud_step", 1))
+    max_depth = int(cfg.get("grasp", {}).get("pointcloud_max_depth_mm", 2000))
+    try:
+        with sw.step("pointcloud_roi"):
+            pc, _meta = nuwa.pointcloud_roi(u_min, v_min, u_max, v_max,
+                                            step=step, max_depth_mm=max_depth)
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=f"Nuwa pointcloud ROI failed: {e}")
+    with sw.step("filter_outliers"):
+        cropped = filter_outliers(pc)
+    if len(cropped) > max_points:
+        idx = np.random.choice(len(cropped), max_points, replace=False)
+        cropped = cropped[idx]
+    pts_cv = nuwa_to_cv_frame(cropped[:, :3]) if len(cropped) else np.empty((0, 3), dtype=np.float64)
+    return pts_cv, "bbox_roi", extra
+```
+
+Replace with:
+
+```python
+    u_min, v_min, u_max, v_max = bbox
+    step = int(cfg.get("grasp", {}).get("pointcloud_step", 1))
+    max_depth = int(cfg.get("grasp", {}).get("pointcloud_max_depth_mm", 2000))
+    try:
+        with sw.step("pointcloud_roi"):
+            pc, _meta = nuwa.pointcloud_roi(u_min, v_min, u_max, v_max,
+                                            step=step, max_depth_mm=max_depth)
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=f"Nuwa pointcloud ROI failed: {e}")
+    with sw.step("filter_outliers"):
+        cropped = filter_outliers(pc)
+    pts_cv = nuwa_to_cv_frame(cropped[:, :3]) if len(cropped) else np.empty((0, 3), dtype=np.float64)
+    pts_cv, plane_info = apply_below_plane_filter(pts_cv, get_work_plane())
+    extra["plane_applied"] = plane_info["plane_applied"]
+    extra["n_points_before_plane"] = plane_info["n_points_before_plane"]
+    if len(pts_cv) > max_points:
+        idx = np.random.choice(len(pts_cv), max_points, replace=False)
+        pts_cv = pts_cv[idx]
+    return pts_cv, "bbox_roi", extra
+```
+
+- [ ] **Step 6: Surface plane fields in `/scan` and `/object_points` responses**
+
+In `/scan` (after the `_object_cloud_cv` call), find the section that sets `out["source"] = source` and add the plane fields:
+
+```python
+    out["selected"] = target
+    out["source"] = source
+    if extra.get("sam21_fallback_reason"):
+        out["sam21_fallback_reason"] = extra["sam21_fallback_reason"]
+    if "plane_applied" in extra:
+        out["plane_applied"] = extra["plane_applied"]
+        out["n_points_before_plane"] = extra["n_points_before_plane"]
+    out["n_points"] = int(len(pts_cv))
+```
+
+Do the same in `/object_points`: after `"source": source`, add the same two-key block:
+
+```python
+    if "plane_applied" in extra:
+        out["plane_applied"] = extra["plane_applied"]
+        out["n_points_before_plane"] = extra["n_points_before_plane"]
+```
+
+- [ ] **Step 7: Smoke-test imports and run pytest**
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run python -c "from presa_mano.api.grasp_routes import _object_points_in_base, _points_via_bbox; from presa_mano.api.perception_routes import _object_cloud_cv; from presa_mano.perception.sam21_bridge import points_via_sam21; print('ok')"
+uv run pytest -v
+```
+
+Expected: smoke prints `ok`; all 19 tests pass (11 from before + 8 new from Task 1).
+
+- [ ] **Step 8: Commit**
+
+```bash
+git add src/presa_mano/perception/sam21_bridge.py src/presa_mano/api/grasp_routes.py src/presa_mano/api/perception_routes.py
+git commit -m "feat: apply work-plane filter at all three cloud sites
+
+points_via_sam21, _points_via_bbox, and _object_cloud_cv (bbox branch)
+now call apply_below_plane_filter on the CV-frame cloud before the
+<10-points guard. Responses carry plane_applied + n_points_before_plane.
+Without a saved plane the filter is a no-op (plane_applied=false).
+
+Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Task 3: New `/api/work_plane/*` endpoints
+
+**Files:**
+- Create: `src/presa_mano/api/work_plane_routes.py`
+- Modify: `src/presa_mano/main.py`
+
+- [ ] **Step 1: Create the router module**
+
+Create `src/presa_mano/api/work_plane_routes.py`:
+
+```python
+"""Work-plane calibration endpoints: fit (preview), save, get, delete."""
+from __future__ import annotations
+
+import time
+from dataclasses import asdict
+from typing import Any
+
+import numpy as np
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, Field
+
+from ..perception import filter_outliers, nuwa_to_cv_frame
+from ..perception.work_plane import (
+    WorkPlane,
+    WorkPlaneError,
+    fit_plane_svd,
+    get_work_plane,
+    set_work_plane,
+)
+from ..timing import Stopwatch
+from . import _deps
+
+router = APIRouter()
+
+
+class FitReq(BaseModel):
+    u_min: int = Field(..., ge=0)
+    v_min: int = Field(..., ge=0)
+    u_max: int = Field(..., gt=0)
+    v_max: int = Field(..., gt=0)
+    offset_mm: float = Field(5.0, ge=0.0, le=200.0)
+    max_preview_points: int = Field(5000, ge=100, le=20000)
+
+
+class SaveReq(BaseModel):
+    plane: dict[str, Any]   # accept a serialised WorkPlane
+
+
+@router.post("/fit")
+def fit(req: FitReq) -> dict[str, Any]:
+    """Compute a candidate work plane from a rectangle on the RGB image.
+
+    Does NOT persist. Returns the plane + a downsampled CV-frame cloud for
+    the GUI 3D preview.
+    """
+    sw = Stopwatch()
+    nuwa = _deps.nuwa()
+    try:
+        with sw.step("pointcloud_roi"):
+            pc, _meta = nuwa.pointcloud_roi(
+                req.u_min, req.v_min, req.u_max, req.v_max,
+                step=1, max_depth_mm=4000,
+            )
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=f"Nuwa pointcloud_roi failed: {e}")
+    if len(pc) < 10:
+        raise HTTPException(
+            status_code=422,
+            detail=f"too few depth points in ROI: {len(pc)}",
+        )
+    pts_cv = nuwa_to_cv_frame(pc[:, :3])
+    with sw.step("filter_outliers"):
+        pts_cv = filter_outliers(pts_cv, n_std=2.0)
+    if len(pts_cv) < 10:
+        raise HTTPException(
+            status_code=422,
+            detail=f"too few inliers after outlier filter: {len(pts_cv)}",
+        )
+    try:
+        with sw.step("fit_plane_svd"):
+            normal, offset, centroid, rms_mm = fit_plane_svd(pts_cv)
+    except WorkPlaneError as e:
+        raise HTTPException(status_code=422, detail=str(e))
+
+    wp = WorkPlane(
+        normal=[float(x) for x in normal],
+        offset=float(offset),
+        centroid=[float(x) for x in centroid],
+        n_inliers=int(len(pts_cv)),
+        fit_rms_mm=float(rms_mm),
+        roi=[int(req.u_min), int(req.v_min), int(req.u_max), int(req.v_max)],
+        offset_mm=float(req.offset_mm),
+        created_at=time.time(),
+    )
+
+    # Downsample for preview.
+    if len(pts_cv) > req.max_preview_points:
+        idx = np.random.choice(len(pts_cv), req.max_preview_points, replace=False)
+        preview = pts_cv[idx]
+    else:
+        preview = pts_cv
+
+    return {
+        "plane": asdict(wp),
+        "preview": True,
+        "points_camera": preview.tolist(),
+        "timings_ms": sw.as_dict(),
+    }
+
+
+@router.post("/save")
+def save(req: SaveReq) -> dict[str, Any]:
+    """Persist a previously-fitted work plane to disk."""
+    try:
+        d = dict(req.plane)
+        d.setdefault("offset_mm", 5.0)
+        wp = WorkPlane(**d)
+    except Exception as e:
+        raise HTTPException(status_code=422, detail=f"invalid plane payload: {e}")
+    set_work_plane(wp)
+    return {"ok": True, "plane": asdict(wp), "saved": True}
+
+
+@router.get("")
+def get_current() -> dict[str, Any]:
+    wp = get_work_plane()
+    return {"plane": asdict(wp) if wp is not None else None}
+
+
+@router.delete("")
+def delete_current() -> dict[str, Any]:
+    set_work_plane(None)
+    return {"ok": True, "plane": None}
+```
+
+- [ ] **Step 2: Register the router in main.py**
+
+Open `src/presa_mano/main.py`. Find the existing router-registration block (it calls `app.include_router(...)` for each routes module). Add:
+
+```python
+from .api import work_plane_routes
+app.include_router(work_plane_routes.router, prefix="/api/work_plane", tags=["work_plane"])
+```
+
+Place it next to the other `include_router` calls (alphabetical or following the order used in the file — match existing style).
+
+- [ ] **Step 3: Smoke-test in process**
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run python -c "from presa_mano.main import app; print([r.path for r in app.routes if '/work_plane' in r.path])"
+```
+
+Expected output: four routes listed:
+
+```
+['/api/work_plane/fit', '/api/work_plane/save', '/api/work_plane', '/api/work_plane']
+```
+
+- [ ] **Step 4: Run pytest**
+
+```bash
+uv run pytest -v
+```
+
+Expected: still 19 PASS (Task 3 adds no tests; we'll verify endpoints live in Task 6).
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add src/presa_mano/api/work_plane_routes.py src/presa_mano/main.py
+git commit -m "feat(api): /api/work_plane/{fit,save,get,delete} endpoints
+
+POST /fit returns a candidate WorkPlane + a downsampled CV-frame cloud
+preview, without writing to disk. POST /save persists the plane. GET
+returns the current saved plane (or null). DELETE clears it.
+
+Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Task 4: Remove the visual-servo feature
+
+Delete the old visual-servo code from both backend and frontend. The plane endpoints under `/api/grasp/visual_servo/plane*` are gone; the new tab in Task 5 will use `/api/work_plane/*`.
+
+**Files:**
+- Modify: `src/presa_mano/api/grasp_routes.py`
+- Delete: `src/presa_mano/grasp/visual_servo.py`
+- Delete: `web/servo.js`
+- Modify: `web/index.html`
+- Modify: `src/presa_mano/main.py`
+
+- [ ] **Step 1: Strip visual-servo code from grasp_routes.py**
+
+Open `src/presa_mano/api/grasp_routes.py`. Remove the following:
+
+1. The import block from `..grasp.visual_servo` (entire `from ..grasp.visual_servo import (...)` group).
+2. The Pydantic models `VisualServoReq`, `VisualServoStepReq`, `PlaneFitReq`.
+3. The helper `_servo_plan_from_req`.
+4. The endpoints decorated with these paths:
+   - `@router.post("/visual_servo/plan")`
+   - `@router.post("/visual_servo/step")`
+   - `@router.post("/visual_servo/plane/fit")`
+   - `@router.get("/visual_servo/plane")`
+   - `@router.delete("/visual_servo/plane")`
+
+To verify exactly which lines to remove, run:
+
+```bash
+grep -n 'visual_servo\|VisualServo\|PlaneFitReq\|WorkPlane\|fit_plane_svd\|set_work_plane\|get_work_plane' src/presa_mano/api/grasp_routes.py
+```
+
+Every match should either be (a) one of the lines above to delete, or (b) the *already-correct* line `from ..perception.work_plane import apply_below_plane_filter, get_work_plane` (added in Task 2). The line `import time` and `from dataclasses import asdict` may become unused after the deletion — drop them if so.
+
+- [ ] **Step 2: Verify grasp_routes still imports**
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run python -c "from presa_mano.api.grasp_routes import router, _object_points_in_base, _points_via_bbox; print('ok')"
+```
+
+Expected: `ok`. If you get `ImportError` for a missing symbol, you removed too much; restore as needed.
+
+- [ ] **Step 3: Delete `grasp/visual_servo.py`**
+
+```bash
+git rm src/presa_mano/grasp/visual_servo.py
+```
+
+Re-run the smoke import to confirm nothing else depended on it:
+
+```bash
+uv run python -c "from presa_mano.main import app; print('ok')"
+```
+
+Expected: `ok`. If you see `ModuleNotFoundError: 'presa_mano.grasp.visual_servo'`, search for the lingering import:
+
+```bash
+grep -rn 'visual_servo' src/presa_mano/
+```
+
+…and remove each one.
+
+- [ ] **Step 4: Delete `web/servo.js`**
+
+```bash
+git rm web/servo.js
+```
+
+- [ ] **Step 5: Strip the servo tab from index.html**
+
+In `web/index.html`:
+
+1. Find the line `<button class="tab" data-tab="servo">Servo Visuale</button>` and delete it.
+2. Find the section `<section class="panel" id="tab-servo">` and delete the entire section (from `<section ... id="tab-servo">` up to and including the matching `</section>`). The section spans roughly lines 237–316 in the current file.
+3. Find the script tag `<script type="module" src="/static/servo.js"></script>` (near the bottom) and delete it.
+
+(Task 5 will add a new tab button and panel for "Calibrazione Piano" plus the new script tag. In this task we just remove.)
+
+- [ ] **Step 6: Update main.py cache-bust list**
+
+Open `src/presa_mano/main.py` and find the cache-bust loop. Currently:
+
+```python
+    for name in ("app.js", "servo.js", "style.css"):
+```
+
+Change to (anticipating Task 5):
+
+```python
+    for name in ("app.js", "work_plane.js", "style.css"):
+```
+
+- [ ] **Step 7: Run pytest + smoke-test the running app builds**
+
+```bash
+cd /opt/docker/robotserver/presa-mano
+uv run pytest -v
+uv run python -c "from presa_mano.main import app; print(sorted([r.path for r in app.routes if r.path.startswith('/api/')]))"
+```
+
+Expected: 19 PASS. The route list should NOT contain any `/api/grasp/visual_servo/...` entry, but should contain `/api/work_plane/*` and the unchanged `/api/grasp/{plan,execute}` etc.
+
+- [ ] **Step 8: Commit**
+
+```bash
+git add -u src/presa_mano/api/grasp_routes.py src/presa_mano/main.py web/index.html
+git commit -m "refactor: remove visual-servo feature
+
+The marker-relative servo (endpoints /api/grasp/visual_servo/{plan,step,
+plane/*}, helpers in grasp_routes.py, the bulk of grasp/visual_servo.py,
+web/servo.js, and the Servo Visuale GUI tab) is gone. Plane fitting now
+lives in /api/work_plane/* and perception/work_plane.py; Task 5 wires the
+new Calibrazione Piano tab.
+
+Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Task 5: New "Calibrazione Piano" tab
+
+Add the tab button and panel to `index.html`, plus a fresh `work_plane.js` with ROI drawing, fit preview, offset slider, save/clear, and a 3D viewer with the plane mesh and color-coded scatter.
+
+**Files:**
+- Modify: `web/index.html`
+- Create: `web/work_plane.js`
+
+- [ ] **Step 1: Add the tab button**
+
+In `web/index.html`, find the tab nav (`<button class="tab" ...>` lines near line 15). Insert a new button after the "Impostazioni AI" tab (and where the old "Servo Visuale" used to be):
+
+```html
+    <button class="tab" data-tab="workplane">Calibrazione Piano</button>
+```
+
+- [ ] **Step 2: Add the panel section**
+
+In `web/index.html`, immediately after the `</section>` that closes `#tab-ai-settings` (or wherever the previous `#tab-servo` section used to be), insert:
+
+```html
+<!-- ============================== CALIBRAZIONE PIANO ============================== -->
+<section class="panel" id="tab-workplane">
+  <div class="diag3d-toolbar">
+    <span class="post-health" id="wpStatusChip">—</span>
+  </div>
+  <div class="grid-2 diag3d-layout">
+    <div>
+      <div id="wpViewport" style="position:relative;width:min(100%,720px);height:440px;overflow:hidden;background:#000;border:1px solid var(--border);border-radius:4px;cursor:grab;user-select:none">
+        <div id="wpPanZoom" style="position:absolute;left:0;top:0;transform-origin:0 0;will-change:transform">
+          <img id="wpImg" alt="Nuwa RGB" style="display:block;max-width:none;pointer-events:none" draggable="false" />
+          <canvas id="wpOverlay" style="position:absolute;left:0;top:0;pointer-events:none"></canvas>
+        </div>
+      </div>
+      <div class="marker-info">RGB live · shift+drag = ROI · drag = pan · wheel = zoom · doppio click = reset</div>
+      <h3 style="margin-top:14px">Vista 3D (frame camera)</h3>
+      <div class="canvas-wrap" style="width:min(100%,720px)">
+        <canvas id="wp3dCanvas" width="720" height="360" style="display:block;background:#0a0d11;border:1px solid var(--border);border-radius:4px"></canvas>
+      </div>
+      <div class="marker-info">piano viola · verde = inlier · giallo = sopra piano+offset · rosso = sotto piano-offset · grigio = buffer · drag = ruota · wheel = zoom</div>
+    </div>
+    <div>
+      <h2>Calibrazione</h2>
+      <div class="ctrl-block">
+        <div class="info-box">
+          Disegna con shift+drag un rettangolo sul tavolo (senza oggetti), poi
+          clicca <strong>Calcola piano</strong>. Regola l'offset finché i punti
+          gialli rappresentano l'oggetto e i rossi spariscono. Poi <strong>Salva piano</strong>.
+        </div>
+        <div class="kv compact">
+          <label>ROI <span id="wpRoiInfo" class="dim">— · disegna con shift+drag</span></label>
+          <label>offset (mm) <input type="number" id="wpOffset" value="5.0" step="0.5" min="0" max="200" /></label>
+        </div>
+        <div class="actions">
+          <button id="wpFitBtn" class="primary" disabled>Calcola piano (preview)</button>
+          <button id="wpSaveBtn" disabled>Salva piano</button>
+          <button id="wpClearBtn" class="danger">Rimuovi piano salvato</button>
+        </div>
+      </div>
+
+      <h2>Stato</h2>
+      <div id="wpStateBlock" class="ctrl-block">
+        <div class="dim">nessun piano</div>
+      </div>
+
+      <h2>Dettagli piano</h2>
+      <pre id="wpDetails" class="log small">—</pre>
+    </div>
+  </div>
+</section>
+```
+
+- [ ] **Step 3: Add the script tag at the bottom of index.html**
+
+Near the bottom of `web/index.html`, find the existing `<script src="/static/app.js"></script>`. After that line, add:
+
+```html
+<script type="module" src="/static/work_plane.js"></script>
+```
+
+- [ ] **Step 4: Create `web/work_plane.js`**
+
+Create `web/work_plane.js`:
+
+```javascript
+// Calibrazione Piano tab: shift+drag ROI on RGB, fit preview, offset
+// slider, save / clear, and a 3D scatter with the plane mesh.
+
+import * as THREE from 'https://unpkg.com/three@0.160.0/build/three.module.js';
+import { OrbitControls } from 'https://unpkg.com/three@0.160.0/examples/jsm/controls/OrbitControls.js';
+
+const $ = (id) => document.getElementById(id);
+const RGB_REFRESH_MS = 1000;
+
+let roi = null;            // {u0,v0,u1,v1} in image pixel coords
+let previewPlane = null;   // last response.plane (preview, not saved)
+let previewPoints = null;  // Float32Array(N*3) in CV camera frame, meters
+let savedPlane = null;     // last GET /api/work_plane response.plane
+
+function setChip(text, level) {
+  const el = $('wpStatusChip');
+  if (!el) return;
+  el.textContent = text;
+  el.className = 'post-health ' + (level || '');
+}
+
+function setRoiInfo() {
+  const el = $('wpRoiInfo');
+  if (!roi) { el.textContent = '— · disegna con shift+drag'; el.classList.add('dim'); return; }
+  el.classList.remove('dim');
+  el.textContent = `u=${roi.u0}…${roi.u1} v=${roi.v0}…${roi.v1} (${roi.u1 - roi.u0}×${roi.v1 - roi.v0} px)`;
+}
+
+function updateButtonStates() {
+  $('wpFitBtn').disabled = !roi;
+  $('wpSaveBtn').disabled = !previewPlane;
+  $('wpClearBtn').disabled = !savedPlane;
+}
+
+function refreshRgb() {
+  $('wpImg').src = `/api/perception/rgb?t=${Date.now()}`;
+  // Fallback: many builds expose Nuwa RGB through `/static/rgb` or similar.
+  // If the above 404s the user sees a broken image; that's a known
+  // limitation of perception_routes.py not exposing a GUI RGB endpoint.
+}
+
+// Note: presa-mano serves Nuwa RGB indirectly. The existing GUI uses the
+// upstream URL via `/api/perception/scan` for detection-based refreshes;
+// for the static viewer we proxy through Nuwa's own endpoint. Adjust the
+// path below if your deployment routes differently.
+function rgbUrl() {
+  // Nuwa is exposed on the host network (per docker-compose), and the GUI
+  // is same-origin to presa-mano (8890). We can't reach :8877 directly from
+  // a browser unless the user opens it; instead use the proxy that already
+  // exists: GET /api/perception/measure backs Nuwa RGB? No — there is no
+  // RGB proxy in presa-mano. Use the existing servo.js trick: hit Nuwa
+  // through its own URL using window.location.hostname.
+  const host = window.location.hostname;
+  return `http://${host}:8877/api/rgb?t=${Date.now()}`;
+}
+
+function startRgbLoop() {
+  const tick = () => { $('wpImg').src = rgbUrl(); };
+  tick();
+  setInterval(tick, RGB_REFRESH_MS);
+}
+
+// ---------------- ROI drawing on the RGB image ----------------
+(function setupRoiDrawing() {
+  const viewport = $('wpViewport');
+  const img = $('wpImg');
+  const overlay = $('wpOverlay');
+  let drawing = null;
+
+  function imgPixelFromEvent(e) {
+    const rect = img.getBoundingClientRect();
+    const sx = img.naturalWidth / rect.width;
+    const sy = img.naturalHeight / rect.height;
+    return {
+      u: Math.round((e.clientX - rect.left) * sx),
+      v: Math.round((e.clientY - rect.top) * sy),
+    };
+  }
+
+  function syncOverlaySize() {
+    overlay.width = img.naturalWidth || 640;
+    overlay.height = img.naturalHeight || 480;
+    overlay.style.width = img.clientWidth + 'px';
+    overlay.style.height = img.clientHeight + 'px';
+  }
+  img.addEventListener('load', syncOverlaySize);
+
+  function drawOverlay() {
+    const ctx = overlay.getContext('2d');
+    ctx.clearRect(0, 0, overlay.width, overlay.height);
+    if (roi) {
+      ctx.strokeStyle = '#9b6dff';
+      ctx.lineWidth = 2;
+      ctx.strokeRect(roi.u0, roi.v0, roi.u1 - roi.u0, roi.v1 - roi.v0);
+    }
+  }
+
+  viewport.addEventListener('mousedown', (e) => {
+    if (!e.shiftKey) return;   // shift+drag = ROI; plain drag = pan (no-op here)
+    const p = imgPixelFromEvent(e);
+    drawing = { u0: p.u, v0: p.v, u1: p.u, v1: p.v };
+    e.preventDefault();
+  });
+  viewport.addEventListener('mousemove', (e) => {
+    if (!drawing) return;
+    const p = imgPixelFromEvent(e);
+    drawing.u1 = p.u;
+    drawing.v1 = p.v;
+    roi = {
+      u0: Math.min(drawing.u0, drawing.u1),
+      v0: Math.min(drawing.v0, drawing.v1),
+      u1: Math.max(drawing.u0, drawing.u1),
+      v1: Math.max(drawing.v0, drawing.v1),
+    };
+    drawOverlay();
+    setRoiInfo();
+    updateButtonStates();
+  });
+  viewport.addEventListener('mouseup', () => { drawing = null; });
+  viewport.addEventListener('mouseleave', () => { drawing = null; });
+})();
+
+// ---------------- Three.js 3D viewer ----------------
+const three = (function setupThree() {
+  const canvas = $('wp3dCanvas');
+  const scene = new THREE.Scene();
+  const camera = new THREE.PerspectiveCamera(45, canvas.width / canvas.height, 0.01, 20);
+  camera.position.set(0.5, -0.5, -1.2);
+  camera.lookAt(0, 0, 1);
+  const renderer = new THREE.WebGLRenderer({ canvas, antialias: true });
+  renderer.setSize(canvas.width, canvas.height);
+  const controls = new OrbitControls(camera, canvas);
+  scene.add(new THREE.AxesHelper(0.1));
+  const planeMesh = new THREE.Mesh(
+    new THREE.PlaneGeometry(0.4, 0.4),
+    new THREE.MeshBasicMaterial({ color: 0x9b6dff, transparent: true, opacity: 0.3, side: THREE.DoubleSide }),
+  );
+  planeMesh.visible = false;
+  scene.add(planeMesh);
+  let scatter = null;
+
+  function render() { renderer.render(scene, camera); }
+  (function loop() { controls.update(); render(); requestAnimationFrame(loop); })();
+
+  function setPlane(plane) {
+    if (!plane) { planeMesh.visible = false; return; }
+    const c = plane.centroid;
+    const n = plane.normal;
+    planeMesh.position.set(c[0], c[1], c[2]);
+    // Align plane to its normal.
+    planeMesh.lookAt(c[0] + n[0], c[1] + n[1], c[2] + n[2]);
+    planeMesh.visible = true;
+  }
+
+  function setScatter(pointsCv, plane, offsetMm) {
+    if (scatter) { scene.remove(scatter); scatter.geometry.dispose(); scatter.material.dispose(); scatter = null; }
+    if (!pointsCv || pointsCv.length === 0) return;
+    const n = pointsCv.length;
+    const positions = new Float32Array(n * 3);
+    const colors = new Float32Array(n * 3);
+    const nx = plane ? plane.normal[0] : 0;
+    const ny = plane ? plane.normal[1] : 0;
+    const nz = plane ? plane.normal[2] : 0;
+    const d = plane ? plane.offset : 0;
+    const offsetM = (offsetMm || 0) / 1000.0;
+    const rmsM = plane ? (plane.fit_rms_mm / 1000.0) * 2.0 : 0.01;
+    for (let i = 0; i < n; i++) {
+      const x = pointsCv[i][0], y = pointsCv[i][1], z = pointsCv[i][2];
+      positions[3 * i] = x; positions[3 * i + 1] = y; positions[3 * i + 2] = z;
+      const signed = plane ? (nx * x + ny * y + nz * z + d) : 0;
+      let r = 0.5, g = 0.5, b = 0.5;
+      if (plane) {
+        if (signed > offsetM)       { r = 1.0; g = 0.83; b = 0.31; }   // yellow: above plane+offset
+        else if (signed < -offsetM) { r = 0.93; g = 0.27; b = 0.27; }  // red: below plane-offset
+        else if (Math.abs(signed) <= rmsM) { r = 0.30; g = 0.78; b = 0.40; }  // green: inlier
+        else                        { r = 0.55; g = 0.55; b = 0.55; }  // grey: buffer
+      }
+      colors[3 * i] = r; colors[3 * i + 1] = g; colors[3 * i + 2] = b;
+    }
+    const geom = new THREE.BufferGeometry();
+    geom.setAttribute('position', new THREE.BufferAttribute(positions, 3));
+    geom.setAttribute('color', new THREE.BufferAttribute(colors, 3));
+    const mat = new THREE.PointsMaterial({ size: 0.005, vertexColors: true });
+    scatter = new THREE.Points(geom, mat);
+    scene.add(scatter);
+  }
+
+  return { setPlane, setScatter };
+})();
+
+// ---------------- State + button handlers ----------------
+async function fetchSaved() {
+  try {
+    const r = await fetch('/api/work_plane').then((r) => r.json());
+    savedPlane = r.plane || null;
+  } catch (e) {
+    savedPlane = null;
+  }
+  if (savedPlane && !previewPlane) {
+    three.setPlane(savedPlane);
+    renderDetails(savedPlane);
+    $('wpStateBlock').innerHTML = `piano salvato · RMS ${savedPlane.fit_rms_mm.toFixed(1)} mm · n_inliers ${savedPlane.n_inliers} · offset ${savedPlane.offset_mm} mm`;
+    setChip('piano salvato', 'ok');
+  } else if (!savedPlane && !previewPlane) {
+    $('wpStateBlock').innerHTML = '<div class="dim">nessun piano</div>';
+    setChip('nessun piano');
+  }
+  updateButtonStates();
+}
+
+function renderDetails(plane) {
+  $('wpDetails').textContent = JSON.stringify(plane, null, 2);
+}
+
+async function doFit() {
+  if (!roi) return;
+  setChip('fit…');
+  $('wpFitBtn').disabled = true;
+  try {
+    const body = {
+      u_min: roi.u0, v_min: roi.v0, u_max: roi.u1, v_max: roi.v1,
+      offset_mm: parseFloat($('wpOffset').value || '5'),
+    };
+    const r = await fetch('/api/work_plane/fit', {
+      method: 'POST', headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(body),
+    }).then((r) => r.json());
+    if (r.detail) throw new Error(r.detail);
+    previewPlane = r.plane;
+    previewPoints = r.points_camera;
+    three.setPlane(previewPlane);
+    three.setScatter(previewPoints, previewPlane, previewPlane.offset_mm);
+    renderDetails(previewPlane);
+    $('wpStateBlock').innerHTML = `<strong>preview</strong> · RMS ${previewPlane.fit_rms_mm.toFixed(1)} mm · n_inliers ${previewPlane.n_inliers} · offset ${previewPlane.offset_mm} mm · <span class="warn">non salvato</span>`;
+    setChip('preview pronto', 'ok');
+  } catch (e) {
+    setChip('fit failed: ' + e.message, 'err');
+  } finally {
+    updateButtonStates();
+  }
+}
+
+async function doSave() {
+  if (!previewPlane) return;
+  setChip('save…');
+  try {
+    const r = await fetch('/api/work_plane/save', {
+      method: 'POST', headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ plane: previewPlane }),
+    }).then((r) => r.json());
+    if (r.detail) throw new Error(r.detail);
+    savedPlane = r.plane;
+    previewPlane = null;
+    $('wpStateBlock').innerHTML = `piano salvato · RMS ${savedPlane.fit_rms_mm.toFixed(1)} mm · n_inliers ${savedPlane.n_inliers} · offset ${savedPlane.offset_mm} mm`;
+    renderDetails(savedPlane);
+    setChip('piano salvato', 'ok');
+  } catch (e) {
+    setChip('save failed: ' + e.message, 'err');
+  } finally {
+    updateButtonStates();
+  }
+}
+
+async function doClear() {
+  if (!confirm('Rimuovere il piano salvato?')) return;
+  setChip('clear…');
+  try {
+    await fetch('/api/work_plane', { method: 'DELETE' }).then((r) => r.json());
+    savedPlane = null;
+    previewPlane = null;
+    previewPoints = null;
+    three.setPlane(null);
+    three.setScatter(null);
+    $('wpStateBlock').innerHTML = '<div class="dim">nessun piano</div>';
+    $('wpDetails').textContent = '—';
+    setChip('nessun piano');
+  } catch (e) {
+    setChip('clear failed: ' + e.message, 'err');
+  } finally {
+    updateButtonStates();
+  }
+}
+
+// ---------------- Offset slider: re-color in real time ----------------
+$('wpOffset').addEventListener('input', () => {
+  if (previewPlane && previewPoints) {
+    const off = parseFloat($('wpOffset').value || '0');
+    previewPlane.offset_mm = off;
+    three.setScatter(previewPoints, previewPlane, off);
+    $('wpStateBlock').innerHTML = `<strong>preview</strong> · RMS ${previewPlane.fit_rms_mm.toFixed(1)} mm · n_inliers ${previewPlane.n_inliers} · offset ${previewPlane.offset_mm.toFixed(1)} mm · <span class="warn">non salvato</span>`;
+  }
+});
+
+$('wpFitBtn').addEventListener('click', doFit);
+$('wpSaveBtn').addEventListener('click', doSave);
+$('wpClearBtn').addEventListener('click', doClear);
+
+// Initial load.
+startRgbLoop();
+fetchSaved();
+```
+
+- [ ] **Step 5: Smoke build the Docker image**
+
+```bash
+docker compose -f /opt/docker/robotserver/docker-compose.yml up -d --build presa-mano
+until curl -fsS http://localhost:8890/health >/dev/null 2>&1; do sleep 2; done
+echo healthy
+```
+
+Expected: container builds and reports healthy.
+
+- [ ] **Step 6: Manual GUI smoke**
+
+Open `http://localhost:8890/` in a browser and hard-refresh (`Ctrl+Shift+R`). Verify:
+
+1. The tab bar contains "Calibrazione Piano" (no more "Servo Visuale").
+2. Clicking it shows the new panel with the RGB image (live), a 3D canvas with axes only, and three buttons.
+3. `GET /api/work_plane` is called on load and returns `{plane: null}` (or the existing saved plane if any).
+
+If anything is broken: open DevTools, check the Console for errors. Common issues:
+- Three.js import failed: the unpkg URLs may be blocked offline. Use a local copy if needed.
+- RGB image 404: the `rgbUrl()` helper assumes Nuwa is reachable at `host:8877`. If the deployment routes differently, adjust.
+
+- [ ] **Step 7: Commit**
+
+```bash
+git add web/index.html web/work_plane.js
+git commit -m "feat(gui): Calibrazione Piano tab (ROI + fit preview + 3D viewer)
+
+New tab replaces the removed Servo Visuale. Shift+drag on the RGB image
+defines a rectangle, 'Calcola piano' calls POST /api/work_plane/fit and
+renders the plane + a colored scatter in 3D. Adjusting the offset slider
+re-colors the scatter client-side. 'Salva piano' persists; 'Rimuovi piano
+salvato' clears.
+
+Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>"
+```
+
+---
+
+## Task 6: Live integration verification
+
+Verify the full flow end-to-end against a running stack (Nuwa + SAM21 + presa-mano + at least one object on the table).
+
+**Files:** none modified.
+
+- [ ] **Step 1: Ensure all upstream services are healthy**
+
+```bash
+curl -fsS http://localhost:8877/api/health && echo " | nuwa ok"
+curl -fsS http://localhost:8002/api/health && echo " | sam21 ok"
+curl -fsS http://localhost:8879/health && echo " | yolo ok"
+curl -fsS http://localhost:8890/health && echo " | presa-mano ok"
+```
+
+Expected: all four print `ok`.
+
+- [ ] **Step 2: Verify a clean baseline (no plane)**
+
+```bash
+curl -s -X DELETE http://localhost:8890/api/work_plane | jq .
+curl -s -X POST http://localhost:8890/api/grasp/plan -H 'Content-Type: application/json' -d '{}' \
+  | jq '{source, n_points, plane_applied, n_points_before_plane}'
+```
+
+Expected: `plane: null` from delete. `/grasp/plan` returns `plane_applied: false`, `n_points_before_plane == n_points` (no filtering).
+
+- [ ] **Step 3: Calibrate a plane from the GUI**
+
+In the browser:
+
+1. Open the "Calibrazione Piano" tab.
+2. Place a flat region of the table in view (move any objects out of a corner of the frame).
+3. Shift+drag a rectangle over the bare table area.
+4. Click "Calcola piano (preview)". Expected: 3D view shows the plane mesh and a tight green cluster of inliers along it; RMS is < 5 mm.
+5. Click "Salva piano".
+
+Verify via API:
+
+```bash
+curl -s http://localhost:8890/api/work_plane | jq .
+ls -la /opt/docker/robotserver/presa-mano/data/work_plane.json
+```
+
+Expected: GET returns the saved plane; the JSON file exists on disk.
+
+- [ ] **Step 4: Verify the filter on the grasp pipeline**
+
+Put an object on the table:
+
+```bash
+curl -s -X POST http://localhost:8890/api/grasp/plan -H 'Content-Type: application/json' -d '{}' \
+  | jq '{source, n_points, plane_applied, n_points_before_plane, sam21_fallback_reason}'
+```
+
+Expected:
+- `source: "sam21"`
+- `plane_applied: true`
+- `n_points` < `n_points_before_plane` (table surface points are dropped)
+- Typical: a Mortaio segmented to ~2400 points before plane, ~1500 after (depending on object height and offset).
+
+- [ ] **Step 5: Verify the filter on /scan and /object_points**
+
+```bash
+curl -s -X POST http://localhost:8890/api/perception/scan -H 'Content-Type: application/json' -d '{"include_points":true}' \
+  | jq '{source, n_points, plane_applied, n_points_before_plane}'
+curl -s -X POST http://localhost:8890/api/perception/object_points -H 'Content-Type: application/json' -d '{}' \
+  | jq '{source, n_points, plane_applied, n_points_before_plane}'
+```
+
+Expected: both responses carry `plane_applied: true` and a non-trivial reduction in `n_points` vs `n_points_before_plane`.
+
+- [ ] **Step 6: Adjust offset and re-fit (preview-only)**
+
+In the GUI, click "Calcola piano" again with the same ROI, change the offset to 20 mm, observe the scatter recolor (more yellow points appear as the threshold shifts up). Note: the recolor happens client-side; `n_points` returned by `/grasp/plan` does NOT change until "Salva piano" is clicked.
+
+Click "Salva piano". Re-run `/api/grasp/plan` and confirm `n_points` increases compared to step 4 (because the more permissive offset keeps more points).
+
+- [ ] **Step 7: Verify clear**
+
+```bash
+curl -s -X DELETE http://localhost:8890/api/work_plane | jq .
+curl -s -X POST http://localhost:8890/api/grasp/plan -H 'Content-Type: application/json' -d '{}' \
+  | jq '{plane_applied, n_points_before_plane, n_points}'
+ls -la /opt/docker/robotserver/presa-mano/data/work_plane.json 2>&1 | head -1
+```
+
+Expected: `plane_applied: false`, `n_points == n_points_before_plane`, the JSON file is gone.
+
+- [ ] **Step 8: Tail logs for ERROR-level lines**
+
+```bash
+docker compose -f /opt/docker/robotserver/docker-compose.yml logs --since=5m presa-mano | grep -E 'ERROR|Traceback' || echo "no errors"
+```
+
+Expected: `no errors`.
+
+- [ ] **Step 9: Final commit (only if anything new emerged)**
+
+If verification revealed a real bug that needed fixing during this task, commit that fix here. Otherwise, this task ends without a commit and the feature is done.
+
+---
+
+## Done criteria
+
+- All pytest tests pass: `uv run pytest -v` → 19 green.
+- `data/work_plane.json` is created by "Salva piano" and removed by "Rimuovi piano salvato".
+- `/api/grasp/plan` carries `plane_applied: true` and `n_points < n_points_before_plane` when a plane is saved.
+- The "Servo Visuale" tab and all its endpoints are gone; the "Calibrazione Piano" tab renders correctly with no console errors.
+- No ERROR lines in `presa-mano` logs during normal operation.
+- 6 commits on `main` (or on the feature branch if one is created).