agentsclimarketplace

Geometry

Skill graph-robots/open-robot-skills/tools/geometry

Skill and tool bundles for gap (graph as policy) — Anthropic Agent Skills format, discovered by path

Install
npx -y skills add graph-robots/open-robot-skills --skill geometry

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Pure-math 3D geometry toolbox — back-project masks and depth to point clouds, DBSCAN-filter noise, fit oriented bounding boxes, derive top-down/front grasp poses, and reconstruct collision worlds from RGB-D frames. Use when a workflow needs perception geometry or planner inputs computed on CPU with no model weights.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.9 KB, as published. Nobody here has run it

geometry

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.