perceiving-objects-oneshot
De la autor
Lightweight 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.
Instalare
npx skills add graph-robots/open-robot-skills --skill perceiving-objects-oneshot -a claude-code
Se instalează în .claude/skills/perceiving-objects-oneshot/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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De ce 76
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Skill-uri similare
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- 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
- grounding-dinograph-robots/open-robot-skillsGrounding DINO zero-shot object detection — natural-language queries to labeled 2D bounding boxes with confidence scores. Use when a workflow needs to locate named objects in an RGB image before segmenting or grasping them.Media★ 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-object-partsgraph-robots/open-robot-skillsHierarchical 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.Media★ 58
- tao-train-grounding-dinoNVIDIA/skillsGrounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for language-guided detection — detects objects described by text prompts without a fixed class vocabulary. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Grounding DINO model. Trigger phrases include "train Grounding DINO", "open-vocabulary detection", "text-prompted detector", "language-guided object detection".Altele★ 3.7K
- perceiving-deformable-linear-objectsgraph-robots/open-robot-skillsRead a cable's ordered 3D centreline from one RGB-D frame and keep it current across a task — seed it from an unobstructed survey, re-fit it cold whenever the whole rod is in view, and fall back to tracking the carried prior only when the fresh fit comes back short. Use when a manipulation loop needs the current shape of a deformable linear object (a cable, rope, or hose) once per step rather than every frame, from a fixed third-person camera.Productivitate★ 58
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