| # Scripts |
|
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| Utilities for direct online final-graph runs from an HF checkout. |
|
|
| ```text |
| setup_realtime_final_graph_env.sh # create/update a conda env with RoboCasa, torch, SAM2 code, and OpenAI CLIP |
| verify_realtime_final_graph.py # run one live RoboCasa OpenDrawer final-graph smoke and assert D_node=342 |
| verify_rgb_graph_alignment.py # verify graph/view masks/RGB alignment and saved rgb_manifest round-trip |
| ``` |
|
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| The verifier uses bundled model checkpoints by default: |
|
|
| ```text |
| models/sam2/checkpoints/sam2.1_hiera_base_plus.pt |
| models/clip/ViT-B-32.pt |
| ``` |
|
|
| RoboCasa simulator assets are not redistributed here; follow upstream RoboCasa setup for assets. |
|
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| ## Multi-view RGB / graph alignment contract |
|
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| For joint training, evaluation, and real-time inference, the model-facing online call is: |
|
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| ```python |
| snapshot = extractor.extract_final_graph(visual_backend=visual_backend) |
| graph = snapshot["gnn_graph"] # x [N_real,342], edge_attr [E,8] |
| rgb_frames = snapshot["rgb_frames"] # dict: view_id -> uint8[256,256,3] |
| rgb_frame_cameras = snapshot["rgb_frame_cameras"] # ordered camera names for those view ids |
| ``` |
|
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| All fields in one `snapshot` are produced from the same current simulator state before the policy/env step advances: |
|
|
| ```text |
| current qpos/qvel/contact state |
| + current segmentation renders from the ordered camera list |
| + current RGB renders from the same ordered camera list |
| + current visible RLE masks indexed by (node slot n, view id v) |
| + SAM2 masked pooling on rgb_frames[v] using that same mask |
| = final graph x [N_real,342] |
| ``` |
|
|
| The ordered multi-view camera contract is: |
|
|
| ```text |
| v=0 robot0_agentview_right |
| v=1 robot0_agentview_left |
| v=2 robot0_eye_in_hand |
| ``` |
|
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| Alignment rules for downstream integration: |
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| - `view_visible[n,v]`, `view_centroid[n,v]`, `view_area[n,v]`, `view_bbox[n,v]`, `rle_masks[*]["v"]`, `visual_features_sparse["v"]`, and `rgb_frames[v]` use the same view id. |
| - `visual_features_sparse["n"]` uses the original padded node slot id; `gnn_graph["slot_ids"]` maps active GNN rows back to those padded slots. |
| - `rgb_frames[v]` is an upright `uint8[256,256,3]` RGB frame rendered with the same vertical flip convention as the segmentation/mask path. |
| - `save_realtime_graph_snapshot(snapshot, out_dir)` writes `rgb/view_<v>_<camera>.npy` plus `rgb_manifest.json`, so saved debug/eval snapshots contain graph data first and aligned RGB images alongside it. |
|
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| Alignment gate: |
|
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| ```bash |
| MUJOCO_GL=egl PYOPENGL_PLATFORM=egl python scripts/verify_rgb_graph_alignment.py |
| ``` |
|
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| This uses a deterministic fake visual backend by default so it checks graph/RGB/mask plumbing without requiring SAM2. Use `verify_realtime_final_graph.py --visual-backend sam2` for the real SAM2/CLIP gate. |
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|
