Downloads · 30 days
15
48% of all-time downloads
Dongkkka/task0003-hierarchical-groot-n1d7-80k
task0003-hierarchical-groot-n1d7-80k is a robotics model from Dongkkka. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This is a full, merged inference checkpoint. It combines the temporal F2 GR00T base, the five-class phase predictor, and the 80,000-step jointly trained action head.
Downloads · 30 days
15
48% of all-time downloads
All-time downloads
31
Public
Parameters
3.1B
9.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors9.5 GB · 100%
How the weights are stored.
F321.6B · 51%
From the Hugging Face model README
This is a full, merged inference checkpoint. It combines the temporal F2 GR00T base, the five-class phase predictor, and the 80,000-step jointly trained action head.
The model expects:
[-8, -4, 0];arm_left and arm_right state, eight values per arm;Grasp the trash and drop it into the left trash bin, then grasp the plate and put it into the right blue bin.It predicts 30 action steps for both arms. Execute 5 steps by default, then observe and infer again. Flush any buffered actions when the stabilized phase changes.
Install Isaac-GR00T N1.7 and make this downloaded model directory importable. The included runtime hook creates the phase-prediction modules before Transformers loads the merged weights.
import sys
from pathlib import Path
model_dir = Path("/path/to/downloaded/model")
sys.path.insert(0, str(model_dir))
from hierarchical_runtime import enable_auto_install
enable_auto_install()
from gr00t.policy.gr00t_policy import Gr00tPolicy
policy = Gr00tPolicy(
embodiment_tag="NEW_EMBODIMENT",
model_path=str(model_dir),
device="cuda:0",
)
actions, info = policy.get_action(observation)
Use this interface when phase probabilities, stable phase names, buffer-flush signals, and the recommended execution horizon are required.
import sys
from pathlib import Path
model_dir = Path("/path/to/downloaded/model")
sys.path.insert(0, str(model_dir))
from hierarchical_policy import FullHierarchicalGr00tPolicy
policy = FullHierarchicalGr00tPolicy(
model_path=model_dir,
device="cuda:0",
)
actions, phase_info = policy.get_action(observation)
The underlying VLM dependency is nvidia/Cosmos-Reason2-2B.
Gr00tPolicy compatibility passed;arm_left=(1,30,8), arm_right=(1,30,8);