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GR00T-N1.6-3B — LIBERO All-4 Finetuned

Name: GR00T-N1.6-LIBERO
Availability: InStock

Fine-tuned checkpoint of nvidia/GR00T-N1.6-3B on all four LIBERO task suites (Spatial, Object, Goal, LIBERO-10) with the Franka Panda embodiment.

Evaluation Results

Evaluated with 20 episodes per task across all 4 LIBERO suites (10 tasks × 20 episodes = 200 episodes per suite).

Suite	Success Rate	Episodes
LIBERO-Spatial	96.0%	192 / 200
LIBERO-Object	100.0%	200 / 200
LIBERO-Goal	98.0%	196 / 200
LIBERO-10	97.5%	195 / 200
Average	97.9%	783 / 800

Model Details

Parameter	Value
Architecture	GR00T-N1.6 (`Gr00tN1d6`)
Vision Backbone	`nvidia/Eagle-Block2A-2B-v2`
Backbone Embedding Dim	2048
Hidden Size	1024
Diffusion Layers	32
Diffusion Attention Heads	32
Action Horizon	50
Inference Timesteps	4
Max Action Dim	128
Model Dtype	bfloat16
Embodiment	`libero_panda` (ID: 2)

Training Details

Hyperparameter	Value
Base Checkpoint	`nvidia/GR00T-N1.6-3B`
Training Steps	40,000
Global Batch Size	16
Learning Rate	1e-4
LR Scheduler	Cosine
Warmup Ratio	5%
Weight Decay	1e-5
Tuned Modules	Diffusion model, projector, top 4 LLM layers, VLLN
Frozen Modules	Visual encoder, remaining LLM layers

Usage

python

from gr00t.model.policy import Gr00tPolicy

policy = Gr00tPolicy(
    model_path="0xAnkitSingh/GR00T-N1.6-3B_LIBERO",
    embodiment_tag="LIBERO_PANDA",
    use_sim_policy_wrapper=True,
)

Or serve via the inference server:

bash

uv run python gr00t/eval/run_gr00t_server.py \
    --model-path 0xAnkitSingh/GR00T-N1.6-3B_LIBERO \
    --embodiment-tag LIBERO_PANDA \
    --use-sim-policy-wrapper \
    --port 5556

0xAnkitSingh/GR00T-N1.6-LIBERO

GR00T-N1.6-3B — LIBERO All-4 Finetuned

Evaluation Results

Model Details

Training Details

Usage

0xAnkitSingh/
GR00T-N1.6-LIBERO