feat: initial commit — orchestratore presa-mano con AI assistant + dual-robot

Pipeline completa per calibrazione hand-eye e grasp planning su robot
Unitree G1/H1 con camera depth Nuwa-HP60C. Esposta come FastAPI + GUI vanilla
JS, con assistente AI (OpenRouter + tool calling curato) per pilotare il
sistema in linguaggio naturale.

Architettura:
- FastAPI orchestrator (porta da .env, default 8890) che coordina 4
  microservizi upstream: Nuwa-HP60C, marker-finder, YOLO, robot bridge.
- Dual-robot: services.g1 e services.h1 in config.yaml; switchabili a
  runtime via PUT /api/home/robot.
- Calibrazione hand-eye eye-to-hand split per-side (right/left).
- Marker pose: marker-finder riceve gli intrinseci RGB della Nuwa al boot,
  PnP server-side con quei dati. Nessun re-PnP locale richiesto.
- Depth-to-RGB: registrazione iterativa IR↔RGB per back-projection 3D
  corretta (Nuwa /api/measure usa coords IR; il nostro /api/perception/
  measure_rgb prende pixel RGB e ritorna 3D nel frame RGB OpenCV).
- Correzione affine sul depth bias (calibrabile via marker come
  ground-truth) — riduce errore Z da ~6% a ~1.5% nei test.
- AI assistant: 16 tool curati (move_wrist_*, hand_action, get_marker[_3d],
  detect_object, calibration_*, set_active_robot…), system prompt
  dinamico che riflette robot attivo + marker config + addendum
  customizzabile da GUI. Temperature di default 0.1 per evitare invenzioni.

GUI (7 tab):
- Home: stato comunicazione device + selettore robot attivo G1/H1
- Assistente AI: chat con OpenRouter, history persistita lato browser,
  task riutilizzabili (prompt salvati), trace tool calls
- Impostazioni AI: modello da combo curata, temperature, max iterations,
  prompt addendum, anteprima system prompt
- Calibrazione, Auto-Calibrazione, TrovaUtensile, Servo Visuale, Vista 3D,
  Movimento EE — invariate dalle versioni precedenti

Setup:
- Variabili in presa-mano/.env (template in .env.example)
- OPENROUTER_API_KEY richiesta solo per il tab Assistente AI
- PRESA_MANO_PORT configurabile

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/presa_mano/grasp/axis.py

@@ -0,0 +1,63 @@
+"""Object axis estimation via PCA.
+
+Given Nx3 object points (in any frame), compute:
+  - centroid
+  - 3 principal axes (unit vectors) with corresponding extents
+  - right-handed frame (axes columns form a rotation matrix)
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass
+class ObjectAxes:
+    centroid: np.ndarray              # (3,)
+    axes: np.ndarray                  # (3,3) columns = unit vectors, sorted largest extent first
+    extents: np.ndarray               # (3,) along each axis (meters)
+
+    @property
+    def long_axis(self) -> np.ndarray:
+        return self.axes[:, 0]
+
+    @property
+    def mid_axis(self) -> np.ndarray:
+        return self.axes[:, 1]
+
+    @property
+    def short_axis(self) -> np.ndarray:
+        return self.axes[:, 2]
+
+    def to_dict(self) -> dict:
+        return {
+            "centroid": self.centroid.tolist(),
+            "axes": self.axes.tolist(),
+            "extents_m": self.extents.tolist(),
+        }
+
+
+def compute_axes(points_xyz: np.ndarray) -> ObjectAxes:
+    pts = np.asarray(points_xyz, dtype=np.float64)
+    if pts.ndim != 2 or pts.shape[1] != 3 or len(pts) < 3:
+        raise ValueError(f"need at least 3 3D points, got shape {pts.shape}")
+
+    c = pts.mean(axis=0)
+    centered = pts - c
+    cov = np.cov(centered.T)
+    eigvals, eigvecs = np.linalg.eigh(cov)
+    # eigh returns ascending — reverse for largest-first
+    order = np.argsort(eigvals)[::-1]
+    eigvals = eigvals[order]
+    eigvecs = eigvecs[:, order]
+
+    # Ensure right-handed coordinate system
+    if np.linalg.det(eigvecs) < 0:
+        eigvecs[:, 2] = -eigvecs[:, 2]
+
+    # Extent along each axis = range of projected coordinates
+    proj = centered @ eigvecs  # Nx3
+    extents = proj.max(axis=0) - proj.min(axis=0)
+    return ObjectAxes(centroid=c, axes=eigvecs, extents=extents)