Add ManiFlow plastic/pill step15000 ckpts (R3M+DiTX, [30,41]@30Hz)

README.md

@@ -0,0 +1,55 @@
+---
+license: apache-2.0
+tags:
+  - robotics
+  - imitation-learning
+  - maniflow
+  - ditx
+  - dexterous-manipulation
+library_name: pytorch
+---
+
+# ManiFlow Real-Robot Baseline (plastic / pill)
+
+ManiFlow DiTX image policy on Inspire-G1 real-robot Stage-2 data.
+
+Org: [Humantwin](https://huggingface.co/Humantwin)  
+Repo: [`Humantwin/maniflow`](https://huggingface.co/Humantwin/maniflow)
+
+## Contract
+
+- Action chunk **`[30, 41]`** = Body29 (GMT) + Hand12 (Inspire) @ **30 Hz**
+- Obs: RGB `[B,1,3,H,W]`, state `[B,1,41]`
+- Vision: R3M + DiTX (`n_layer=12`, `n_emb=768`, `visual_cond_len=1024`)
+- Checkpoint: **15k** / 30k
+
+## Layout
+
+```text
+plastic/
+  step15000.ckpt
+  latest.ckpt          # symlink → step15000
+  norm_stats.json
+pill/
+  step15000.ckpt
+  latest.ckpt
+  norm_stats.json
+```
+
+## Train loss @ 15k (approx)
+
+| Task | train loss | val_loss@15k |
+|---|---:|---:|
+| plastic | ~0.05 | (see local logs; val higher) |
+| pill | ~0.08 | lower than plastic |
+
+## Load
+
+```python
+import torch
+from maniflow.deploy.maniflow_robot_worker import ManiFlowRobotWorker
+
+w = ManiFlowRobotWorker("plastic/step15000.ckpt", device="cuda", use_ema=True)
+w.load()
+chunk, ms = w.infer({"rgb": rgb, "state": state})  # chunk: [30,41] raw
+```

deploy_meta.json

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+{
+  "arch": "maniflow_ditx_r3m",
+  "step": 15000,
+  "horizon": 30,
+  "action_dim": 41,
+  "control_hz": 30,
+  "tasks": [
+    "plastic",
+    "pill"
+  ]
+}

pill/norm_stats.json

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pill/step15000.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d90e5012f8dd136acfe75bba1d31b3d0f24bc537fe4aebf4304614bad1c4ed0
+size 1538675437

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