| --- |
| tags: |
| - robotics |
| - human-robot-interaction |
| - flow-matching |
| - robot-learning |
| --- |
| |
| # HRI-diff large, H50, 100k steps |
|
|
| This is the 768-wide HRI-diff joint checkpoint trained for 100,000 steps on |
| HITBench tomato-to-bowl. `model.pt` includes the conditioning encoder, trust |
| flow, action decoder, frozen trust-tube tokenizer, and latent statistics. |
|
|
| The policy takes RGB-D at causal offsets `[-9, -6, -3, 0]`, current 8-D robot |
| state, and separate regular and interaction prompts. It predicts 50 continuous |
| actions. In the closed-loop benchmark it executes one action and replans at |
| every step using 20 flow integration steps. |
|
|
| Evaluation artifacts: https://huggingface.co/datasets/nhatcm/hri_diff_large_eval |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| from hitbench.hitbench.models.hri_diff.training import HRIDiffPhaseTwo |
|
|
| path = snapshot_download("nhatcm/hri_diff_large") |
| model = HRIDiffPhaseTwo.from_pretrained(path, map_location="cpu") |
| ``` |
| |
| The flow-matching dependency is licensed CC BY-NC; check its terms before |
| commercial use. |
| |
