| # Placement Task Asset Notes |
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| This repository is intended to support placement-oriented robotics tasks such as: |
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| - placing objects into containers |
| - placing tableware or food on plates, trays, counters, and shelves |
| - loading objects into articulated fixtures such as dishwashers, drawers, and cabinets |
| - composing tabletop or kitchen scenes from reusable object, receptacle, fixture, robot, and environment assets |
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| ## Currently available local assets |
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| For any imported source, the upstream-to-local directory mapping is recorded in: |
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| ```text |
| docs/structure_mapping.md |
| ``` |
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| ### RoboCasa |
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| RoboCasa currently contributes dishwasher fixtures: |
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| ```text |
| assets/robocasa/raw/fixtures/dishwashers/ |
| assets/robocasa/raw/fixtures/fixture_registry/dishwasher.yaml |
| ``` |
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| These are useful for placement tasks involving loading dishes or objects into a dishwasher. |
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| ### DISCOVERSE |
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| DISCOVERSE is imported as a collection-level asset: |
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| ```text |
| assets/discoverse/raw/collections/models/discoverse_models/ |
| ├── mjcf/ |
| ├── meshes/ |
| └── urdf/ |
| ``` |
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| Placement-relevant DISCOVERSE MJCF task/environment files include: |
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| ```text |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_block.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_coffeecup.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_jujube.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_jujube_coffeecup.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_kiwi_fruit.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/place_spoon.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/coffeecup_plate.xml |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/task_environments/stack_block.xml |
| ``` |
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| Placement-relevant DISCOVERSE object assets include bowls, plates, cups, fruit, boxes, baskets, pans, spoons, towels, trashbins, and cabinets under: |
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| ```text |
| assets/discoverse/raw/collections/models/discoverse_models/mjcf/object/ |
| assets/discoverse/raw/collections/models/discoverse_models/meshes/object/ |
| ``` |
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| ## Candidate external sources |
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| The following sources are recorded as link-only candidates. Do not copy assets from them into this repository until a downstream task actually uses them and the license has been reviewed. |
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| | Source | Link | Placement relevance | Intake rule | |
| | --- | --- | --- | --- | |
| | MuJoCo Menagerie | https://github.com/google-deepmind/mujoco_menagerie | Robot grippers, robot arms, hands, and robot-ready MJCF assets | Import exact used robot/object model with license and upstream commit. | |
| | PartNet-Mobility | https://sapien.ucsd.edu/browse | Articulated cabinets, drawers, doors, and household objects | Import only selected object IDs after checking license and conversion notes. | |
| | Objaverse / Objaverse-XL | https://objaverse.allenai.org | Large-scale everyday objects for object placement and scene variety | Import exact used asset IDs with original UID, license, source URL, and conversion pipeline. | |
| | Google Scanned Objects | https://app.ignitionrobotics.org/GoogleResearch/fuel/collections/Google%20Scanned%20Objects | Scanned household objects suitable for manipulation and placement | Import exact used model with provider URL and license metadata. | |
| | RoboCasa | https://robocasa.ai | Kitchen fixtures, objects, scenes, textures, and placement-style tasks | Import normalized assets under `assets/robocasa/...`. | |
| | DISCOVERSE | https://github.com/discoverse-dev/DISCOVERSE | MJCF task environments, manipulation objects, robots, and scenes | Already imported as `assets/discoverse/raw/collections/models/discoverse_models/`. | |
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| ## Third-party intake checklist |
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| When a placement task uses an external asset: |
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| 1. Copy the exact used asset into this repository. |
| 2. Preserve upstream filenames and relative paths where practical. |
| 3. Add `metadata.yaml` in the asset directory. |
| 4. Add one JSONL row to `manifest/assets.jsonl`. |
| 5. Record license, upstream URL, retrieval date, source asset ID, and conversion steps. |
| 6. Run: |
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| ```bash |
| python scripts/validate_asset.py --all |
| ``` |
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