| # Downstream Graph-WAM Instructions |
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| The next student should treat this dataset as graph-augmented robot trajectory data for DreamZero-style joint vision+action flow matching. |
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| ## Do not assume graph conditioning is the main path |
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| The graph is not assumed to be the main conditioning input. It can be used in several ways, in this recommended order: |
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| 1. Structured modality/state stream: |
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| ```text |
| history RGB + robot state + language + graph state -> joint future vision/action model |
| ``` |
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| 2. Auxiliary ΔG target: |
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| ```text |
| predict future video/action plus relation/event changes |
| ``` |
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| 3. Joint denoised graph modality: |
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| ```text |
| flow match [future video latent, future action, future graph state] |
| ``` |
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| 4. Conditioning ablation only: |
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| ```text |
| same model with graph tokens as extra conditioning |
| ``` |
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| The dataset intentionally supports all of these. |
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| ## Default chunk builder |
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| For chunk starting at `t`: |
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| ```python |
| context_rgb = frames[t-L:t] |
| context_state = robot_state[t-L:t] |
| context_graph = graph_tensors[t-L:t] |
| target_rgb = frames[t:t+H] |
| target_action = action[t:t+H] |
| target_graph_delta = events_or_transition_masks[t:t+H] |
| language = episode.language_instruction |
| ``` |
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| ## Required ablations |
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| - no graph; |
| - graph shuffled across episodes; |
| - stale graph from earlier timestep; |
| - state-tier only; |
| - prior-tier only; |
| - graph as auxiliary target only; |
| - graph as conditioning stream; |
| - graph as joint denoised modality; |
| - right-only vs left-only vs right+wrist vs all three views. |
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| ## Metrics |
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| Use both visual/action metrics and graph-state metrics: |
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| - action MSE / control accuracy / rollout success; |
| - video reconstruction/prediction metrics; |
| - relation event F1; |
| - contact/support/articulation ΔG accuracy; |
| - target-object visibility/ROI metrics; |
| - corruption sensitivity. |
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