geometry

SkillDev tools

Pure-math 3D geometry toolbox — back-project masks and depth to

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the geometry skill

What this skill tells your AI

The instructions your AI receives, as published by graph-robots/open-robot-skills in tools/geometry/SKILL.md and read by ahel’s review.

Pure-math perception/planning geometry as in-process typed tools, from mask back-projection through OBB fitting to grasp-candidate generation, plus the two scalar helpers (geometry.iou, geometry.pose_distance). Fully CPU — no model weights, no GPU.

When to use

  • Turning a segmentation mask + depth + camera calibration into world-frame points (mask_to_world_points) and an object OBB (filter_and_compute_obb).
  • Deriving grasp poses from an OBB: top_down_grasp_candidates for tabletop pick (feed the full list to curobo.plan_to_grasp_poses as a goalset), front_grasp_from_obb for horizontal interactions (drawer/door handles).
  • Building the collision world for the planner: build_world_config with the target's mask in object_masks so the planner can ignore_obstacle_names it.

Install

uv sync --extra geometry   # open3d + scikit-learn (cv2/scipy come with gap core)
# (pip: pip install -e ".[geometry]")

The module imports lazily — the bundle loads (and the light tools work) without the extra; only OBB fitting, DBSCAN filtering and world reconstruction need open3d/sklearn/cv2.

Gotchas (carried over from the service)

  • OBB extent is HALF-extents (gap.types convention, same as the proto). compute_obb is upright-only: rotation is around world Z (no 3D tilt), and extents use the 2nd/98th percentile of points, not strict min/max.
  • Single-camera clouds are 2.5D: only camera-facing surfaces are observed, so OBB centers carry a few cm of depth bias on opaque objects. (The service's rehearsal-sandbox ground-truth snap that compensated for this in-container was deliberately NOT ported — it depended on a /app sandbox file.)
  • top_down_grasp_candidates default z_offset=-0.04: fingertip 4 cm below the OBB top. With z_offset=0.0 the fingers close above the object (silent empty grip). Grasp Z is clamped to -0.05 m (table-clearance floor; LIBERO table top is at world z=0).
  • mask_to_world_points keeps only depths in [0.015, 20.0] m (HyRL bounds); invalid/zero-depth pixels are dropped.
  • filter_noise returns the ORIGINAL cloud unchanged when DBSCAN labels everything noise (defensive fallback, mirrors HyRL).
  • build_world_config: table removal only runs when table_z_threshold != 0 (typical -0.01); robot-point exclusion is Franka-only (simplified DH FK) and skips non-7-DOF joint states; prefer explicit object_masks over the target_obb projection fallback — masks are pixel-accurate, the OBB projection is a corner-AABB approximation inflated by 2 cm.
  • top_down_grasp_from_obb yaw is NOT derived from the OBB — fingers may close across the wide axis; use the candidate fan when orientation matters.

Signals

GitHub stars
41
Forks
7
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
geometry
Source
github.com/graph-robots/open-robot-skills