perceiving-object-parts
De la autor
Hierarchical perception for subpart targeting. Detects a parent object first (DINO+VLM), crops the camera image to the parent's bounding box, then detects and segments the named subpart inside the crop (DINO + SAM3), and uncrops + fuses depth to a world-frame OBB/mask/cloud — plus the parent object's OBB and cloud for downstream placement/collision reasoning. Use when the graspable affordance is a subpart of a larger object — pan handle, drawer pull, mug rim, moka-pot handle, stove burner — where detecting the subpart at full image resolution is unreliable because it occupies few pixels.
Instalare
npx skills add graph-robots/open-robot-skills --skill perceiving-object-parts -a claude-code
Se instalează în .claude/skills/perceiving-object-parts/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 76
Securitate85
Proveniență61
Întreținere100
Licență100
Adopție31
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- perceiving-objectsgraph-robots/open-robot-skillsFast single-path 3D object perception. Runs Grounding-DINO broad detection, a pairwise VLM crop tournament to identify the target box, SAM3 box segmentation, and depth back-projection to a world-frame point cloud, finished by geometry.filter_and_compute_obb for a clean oriented bounding box, mask, and cloud. Multi-camera rigs get a verified wrist-camera fallback gate. Use when a manipulation workflow needs to localize one named object quickly — uncluttered scenes with visually distinct targets, latency-bound loops, or platforms deploying only DINO + VLM + SAM3 + geometry.DevOps★ 58
- perceiving-objects-oneshotgraph-robots/open-robot-skillsLightweight one-shot 3D object perception. Runs Grounding-DINO broad detection, a SINGLE VLM set-of-marks letter pick over the labeled boxes, SAM3 box segmentation, depth back-projection, and geometry.filter_and_compute_obb. No pairwise tournament, no multi-view safe-gate. Returns a clean not_found output (no exception) when the VLM answers "none" or DINO emits no detections — making this the right skill for clean-all-items loops whose natural termination signal is "no more matching objects in view". Use when a multi-item loop needs a clean no-match exit, or for generic target descriptions on uncluttered scenes with reasonably sized targets.Altele★ 58
- perceiving-next-itemgraph-robots/open-robot-skillsLoop-head perception for pack-all / clean-all-items tasks. From ONE observation it localizes BOTH the destination container (basket, bin, box) AND the next remaining target item, using the pairwise VLM crop tournament with a container-excluding description so the target is never confused with the basket, then makes a clean found / none decision: an item was found (grasp it) or only the container remains (all items packed — exit the loop to done). Use when a workflow must pick up EVERY object and place each into a container in a loop (pack-all / clean-all-items) and each pass must reliably answer "is there still a graspable item, or are we done?" while also exposing a fresh container OBB for the downstream place. This subgraph is self-contained and takes NO inputs (inputs: {}); the container and item phrases are literal strings written inside the perception nodes, never subgraph parameters — do not declare item_name / container_name / item_description as subgraph inputs.Redactare★ 58
- perceiving-sorting-pairsgraph-robots/open-robot-skillsDiscovers a labelled four-compartment destination from RGB-D and repeatedly perceives one remaining source object with its matching metric destination region, using an identity table the graph supplies for what each label looks like and what its graspable part is called. Use when a workflow must sort several visible objects into compartments identified by printed category labels.Productivitate★ 58
- transporting-objectsgraph-robots/open-robot-skillsMove the currently-held object to a destination and release. The gripper enters this subgraph holding the object; on exit the object has been placed. For a walled container (basket / bin / box / tote) the default is a straight-Z descent to inside the walls (transport_descend_linear → release); for placement ONTO a surface or into a described sub-region it computes an explicit drop pose (compute_drop → move_above → release). Use after a successful grasp when the destination has a known OBB. When the destination is a sub-region described in natural language (e.g. "the left compartment of the caddy", "to the left of the plate", "the inside of the top drawer"), an optional VLM-grounded perceive_zone state localizes the zone before the drop pose is computed. Heights derive from the perceived container and the live wrist pose rather than a resting tool height, and the top-down orientation from the hand actually on the arm (robot.grasp_frame).Altele★ 58
- robot-perceptionarpitg1304/robotics-agent-skillsComprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.DevOps★ 384
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te