GR00T-N1.5-robocasa-baseline

prehj/GR00T-N1.5-robocasa-baseline

GR00T-N1.5-robocasa-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 RoboCasa Kitchen. This is the reference (baseline, non-MoE) policy used throughout the quantizability gate project, and the policy that every RoboCasa gate module in this collection was measure…

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From the Hugging Face model README

GR00T-N1.5-robocasa-baseline

GR00T-N1.5 finetuned on RoboCasa Kitchen. This is the reference (baseline, non-MoE) policy used throughout the quantizability gate project, and the policy that every RoboCasa gate module in this collection was measured against.

Benchmark: RoboCasa Kitchen, 24 tasks
Embodiment: RoboCasa mobile manipulator. Embodiment tag in the shipped metadata is new_embodiment.
Observations: three 256x256 RGB views — left_view, right_view, wrist_view — at 20 fps
State: base position (3) + base rotation (quaternion, 4) + absolute and relative end-effector position (3+3) and rotation (quaternion, 4+4), plus gripper
Actions: delta end-effector control with a gripper channel, action horizon 16
Finetuning data: MimicGen demonstrations, LeRobot dataset kimtaey/robocasa_mg_gr00t_300
Checkpoint: step 60,000 = 1.85 epochs over the dataset (the run was configured for 2 epochs / 60k steps and 60k came first). Final training loss 0.0324, from 0.577 at step 0.

Measured closed-loop performance

24 tasks x 50 episodes, 4 denoising steps:

configuration	success rate	steps on successful episodes
this policy, uncompressed	0.657	327.0
blanket K=2 action-chunk compression	0.598	214.0

Those two rows are the endpoints of the trade-off that the gate modules in this collection are trying to beat; see for example prehj/quantgate-student-robocasa-phase5-softB.

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	1.85 (60k steps reached first; `num_train_epochs: 2`)
dataloader workers	32
final loss	0.0324

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-robocasa-baseline --local-dir ./ckpt

from gr00t.model.policy import Gr00tPolicy
from gr00t.experiment.data_config import DATA_CONFIG_MAP

data_config = DATA_CONFIG_MAP["robocasa_panda_omron"]
policy = Gr00tPolicy(
    model_path="./ckpt",
    modality_config=data_config.modality_config(),
    modality_transform=data_config.transform(),
    embodiment_tag="new_embodiment",
    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 new_embodiment --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