Grasping by the concavities in a silhouette
SkillDev toolsGrasp an awkward rigid part by the waists in its own silhouette, take the alpha shape of its point cloud from above, find where that boundary turns inward, and close the jaws across a pair of those inward runs. Use when a part has no graspable long axis and no pair of parallel walls an OBB would find, pliers and scissors gripped at the joint, a spool at its neck, a gear between two teeth, a bulb socket at its skirt. Use grasping-linear-feature instead for handles, rods and shafts, and grasping-short-axis when an upright box is enough.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Grasping by the concavities in a silhouette skill
What this skill tells your AI
The instructions your AI receives, as published by graph-robots/open-robot-skills in skills/grasping-concave-contour/SKILL.md and read by ahel’s review.
A parallel jaw wants two opposing surfaces. For a handle or a rod, the pair is
implied by the long axis and grasping-linear-feature finds it. For a part with
no long axis — a pair of pliers, a spur gear, a wire spool, a bulb socket — the
opposing surfaces are the waists: the places where the outline turns inward.
This skill finds them directly.
What the script does
- Projects the segmented cloud to the (x, y) plane the part is seen from.
- Builds the alpha shape of that projection — a Delaunay triangulation
filtered to triangles whose circumradius is under
alpha_radius, whose boundary is the part's real outline rather than its hull. - Walks that boundary and keeps the reflex vertices, where it turns inward. Consecutive reflex vertices are one run; each run's centroid is one candidate pad site.
- Pairs the candidates, keeps the pairs whose secant fits the gripper's span, and builds a straight-down pose centred on each pair's midpoint with the jaws closing along the secant.
- Ranks by confidence and returns the best, with the rest in
candidatesso a caller that fails one can try the next without recomputing.
Where it fits
It is a proposer that also poses: it returns grasp_pose and pregrasp_pose
directly rather than handing a list to a selector. Put verifying-grasps after
it — this skill exits posed with the hand open and does not close it — and
route failed to whatever recovery the graph has, because a part whose
silhouette has no fitting pair of waists is one this skill genuinely cannot take.
A sensible arrangement is to try grasping-linear-feature first and fall through
to this on failed: most parts have an axis, and the ones that do not are
exactly the ones this skill is for.
Signals
- GitHub stars
- 44
- Forks
- 7
- Last commit
- Sep 2026
Advanced
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grasping-concave-contour- Source
- github.com/graph-robots/open-robot-skills