metadata
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
Repo: 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
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
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