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Embodiment tags are used to identify the robot embodiment in your data. They enable GR00T to apply embodiment-specific configurations and handle cross-embodiment training.

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Naming convention

Embodiment tags follow the pattern: 
<dataset>_<robot_name>
When using multiple datasets for the same robot (e.g., sim GR1 and real GR1), you can drop the dataset name and use only the robot name.

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Pretrain embodiment tags

These embodiments were included in the base model pretraining:
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ROBOCASA_PANDA_OMRON
string
default:"robocasa_panda_omron"
The RoboCasa Panda robot with omron mobile base.
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GR1
string
default:"gr1"
The Fourier GR1 robot.

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Pre-registered posttrain embodiment tags

These embodiments have ready-to-use configurations for fine-tuning:
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UNITREE_G1
string
default:"unitree_g1"
The Unitree G1 robot.
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LIBERO_PANDA
string
default:"libero_panda"
The Libero panda robot.
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OXE_GOOGLE
string
default:"oxe_google"
The Open-X-Embodiment Google robot.
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OXE_WIDOWX
string
default:"oxe_widowx"
The Open-X-Embodiment WidowX robot.
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OXE_DROID
string
default:"oxe_droid"
The Open-X-Embodiment DROID robot with relative joint position actions.
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BEHAVIOR_R1_PRO
string
default:"behavior_r1_pro"
The Behavior R1 Pro robot.

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Custom embodiments

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NEW_EMBODIMENT
string
default:"new_embodiment"
Any new embodiment not included in the pre-registered tags.
Use NEW_EMBODIMENT when fine-tuning on your own robot. You’ll need to provide a custom modality configuration.

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Using embodiment tags

Embodiment tags are specified in your dataset and during training/inference:

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In your dataset

Specify the embodiment tag when creating VLAStepData: 
from gr00t.data.embodiment_tags import EmbodimentTag
from gr00t.data.types import VLAStepData

step_data = VLAStepData(
    images={"front": [image_array]},
    states={"joint_pos": state_array},
    actions={"joint_pos": action_array},
    text="pick up the cube",
    embodiment=EmbodimentTag.GR1,  # Specify your robot
    is_demonstration=False,
)

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During training

Specify the embodiment tag in your training command: 
uv run python gr00t/experiment/launch_finetune.py \
    --base-model-path nvidia/GR00T-N1.6-3B \
    --dataset-path <DATASET_PATH> \
    --embodiment-tag UNITREE_G1 \
    --num-gpus 1

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During inference

Specify the embodiment tag when loading the policy: 
uv run python gr00t/eval/run_gr00t_server.py \
    --embodiment-tag GR1 \
    --model-path nvidia/GR00T-N1.6-3B

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Implementation details

Embodiment tags are implemented as an enum in gr00t/data/embodiment_tags.py:14-61: 
from enum import Enum

class EmbodimentTag(Enum):
    ##### Pretrain embodiment tags #####
    ROBOCASA_PANDA_OMRON = "robocasa_panda_omron"
    """
    The RoboCasa Panda robot with omron mobile base.
    """

    GR1 = "gr1"
    """
    The Fourier GR1 robot.
    """

    ##### Pre-registered posttrain embodiment tags #####
    UNITREE_G1 = "unitree_g1"
    """
    The Unitree G1 robot.
    """

    # ... additional embodiments

    # New embodiment during post-training
    NEW_EMBODIMENT = "new_embodiment"
    """
    Any new embodiment.
    """

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Cross-embodiment training

GR00T’s cross-embodiment architecture allows the model to learn from multiple robot types simultaneously. The embodiment tag is used to:
  1. Apply embodiment-specific normalization statistics
  2. Load embodiment-specific modality configurations
  3. Enable the model to distinguish between different robot morphologies
When fine-tuning on a new embodiment, the model leverages knowledge from all pretrained embodiments, enabling faster adaptation with less data.

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Next steps

Modality configs

Configure data processing for your embodiment

Data format

Prepare your data in the correct format

Fine-tuning guide

Fine-tune on your custom embodiment

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