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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
```
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