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GR00T-N1.5-libero-baseline
prehj/GR00T-N1.5-libero-baseline
GR00T-N1.5-libero-baseline is a robotics model from prehj. 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 gr00t. The card lists the license as apache-2.0.
GR00T-N1.5 finetuned on LIBERO. This is the reference (baseline, non-MoE) policy for the LIBERO arm of the quantizability gate project.
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From the Hugging Face model README
GR00T-N1.5-libero-baseline
GR00T-N1.5 finetuned on LIBERO. This is the reference (baseline, non-MoE) policy for the LIBERO arm of the quantizability gate project.
- Benchmark: LIBERO. Closed-loop evaluation in this project runs the
libero_10(LIBERO-Long) suite. - Embodiment: Franka Panda tabletop arm. Embodiment tag in the shipped
metadata is
libero. - Observations: two 256x256 RGB views —
front_view,left_wrist_view— at 10 fps - State: absolute end-effector position (3) + rotation (euler RPY, 3) + gripper state (2)
- Actions: delta end-effector position (3) + delta rotation (euler RPY, 3) + absolute gripper close (1), action horizon 16
- Checkpoint: step 60,000 = 14.04 epochs over the finetuning set (configured for 15 epochs; 60k steps came first). Final training loss 0.0231, from 1.122 at step 0.
The precise composition of the finetuning demonstration set is not recorded
inside the checkpoint — experiment_cfg/metadata.json carries only the
libero embodiment schema and its normalisation statistics. The epoch count
above (14 epochs into 60k steps at batch 64) implies roughly 273k transitions in
the training set.
Architecture
GR00T-N1.5 (model_type: gr00t_n1_5, architectures: ["GR00T_N1_5"]),
unchanged from the NVIDIA release:
| VLM backbone | Eagle — NVEagle/eagle_er-qwen3_1_7B-Siglip2_400M_stage1_5_128gpu_er_v7_1mlp_nops |
| features tapped at | select_layer: 12, backbone embedding dim 2048 |
| backbone tuning | tune_llm: false, tune_visual: true (vision tower trained, LLM frozen) |
| action head | flow-matching DiT, 16 layers, 32 heads, head dim 48, cross-attn dim 2048, interleaved self-attention, ada_norm, dropout 0.2 |
| VL self-attention | 4 layers, 32 heads, head dim 64 |
| action head tuning | tune_diffusion_model: true, tune_projector: true |
| action space | action_dim: 32 (padded), max_state_dim: 64 |
| action horizon | 16 |
| inference denoising steps | 4 |
| dtypes | model_dtype: float32, compute_dtype/torch_dtype: bfloat16 |
Training recipe
Read directly out of this checkpoint's trainer_state.json and the run's
training_args.bin:
| steps | 60,000 (this is the final checkpoint; max_steps == global_step) |
| batch size | 64 effective — 32 per device x 2 GPUs, no gradient accumulation |
| learning rate | 1e-4, cosine schedule, warmup ratio 0.05 |
| weight decay | 1e-5 |
| precision | bf16 |
| epochs | 14.04 (60k steps reached first; num_train_epochs: 15) |
| dataloader workers | 32 |
| final loss | 0.0231 |
How to load and serve
Requires NVIDIA Isaac-GR00T and
transformers==4.51.3 (the version this checkpoint was written with).
huggingface-cli download prehj/GR00T-N1.5-libero-baseline --local-dir ./ckpt
from gr00t.model.policy import Gr00tPolicy
from gr00t.experiment.data_config import DATA_CONFIG_MAP
data_config = DATA_CONFIG_MAP["libero"]
policy = Gr00tPolicy(
model_path="./ckpt",
modality_config=data_config.modality_config(),
modality_transform=data_config.transform(),
embodiment_tag="libero",
device="cuda",
)
action_chunk = policy.get_action(observation) # 16 steps
Or serve it over the Isaac-GR00T inference service and drive it from the simulator client:
python scripts/inference_service.py --server \
--model_path ./ckpt --embodiment_tag libero --denoising_steps 4
experiment_cfg/metadata.json ships in this repo and carries the state/action
normalisation statistics and the modality schema — the policy will not produce
correct actions without it, so keep the directory layout intact.
Files
| file | what it is |
|---|---|
model-0000{1,2}-of-00002.safetensors, model.safetensors.index.json | the weights (~7.1 GB) |
config.json | GR00T-N1.5 architecture config |
experiment_cfg/metadata.json | embodiment schema + normalisation statistics |
trainer_state.json | full 60k-step loss history |
- Task
- Robotics
- Library
- gr00t
- Type
- gr00t_n1_5
- License
- apache-2.0
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026