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This guide demonstrates how to add support for your own robot embodiment in GR00T. The modality configuration defines how your robot’s data should be loaded, processed, and interpreted by the model.

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Overview

Each embodiment requires a Python configuration file that specifies:
  • Which observations to use (video cameras, proprioceptive states)
  • How to sample data temporally (current frame, historical frames, future action horizons)
  • How actions should be interpreted and transformed
  • Which language annotations to use

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Configuration structure

A modality configuration is a Python dictionary containing four top-level keys: "video", "state", "action", and "language". Each key maps to a ModalityConfig object. Here’s the SO-100 example from examples/SO100/so100_config.py: 
from gr00t.configs.data.embodiment_configs import register_modality_config
from gr00t.data.types import ModalityConfig, ActionConfig, ActionRepresentation, ActionType, ActionFormat
from gr00t.data.embodiment_tags import EmbodimentTag

so100_config = {
    "video": ModalityConfig(...),
    "state": ModalityConfig(...),
    "action": ModalityConfig(...),
    "language": ModalityConfig(...),
}

register_modality_config(so100_config, embodiment_tag=EmbodimentTag.NEW_EMBODIMENT)

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Understanding ModalityConfig

Each ModalityConfig specifies two required fields and several optional ones.

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Required fields

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delta_indices (list[int])

Defines which temporal offsets to sample relative to the current timestep. This enables:
  • Historical context: Use negative indices (e.g., [-2, -1, 0]) to include past observations
  • Current observation: Use [0] for the current timestep
  • Future actions: Use positive indices (e.g., list(range(0, 16))) for action prediction horizons
Examples: 
# Single current frame for video
delta_indices=[0]

# Last 3 frames for video (temporal stacking)
delta_indices=[-2, -1, 0]

# 16-step action prediction horizon
delta_indices=list(range(0, 16))
If you modify delta_indices for the action modality (e.g., changing the action horizon from 16 to 8), you must regenerate the dataset statistics by re-running:
python gr00t/data/stats.py <dataset_path> <embodiment_tag>
The normalization statistics are computed based on the original delta_indices length, and a mismatch will cause errors during training.

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modality_keys (list[str])

Specifies which keys to load from your dataset. These keys must match the keys defined in your meta/modality.json file. For the SO-100 example:
  • Video keys: Must match keys in meta/modality.json under "video" (e.g., "front", "wrist")
  • State keys: Must match keys in meta/modality.json under "state" (e.g., "single_arm", "gripper")
  • Action keys: Must match keys in meta/modality.json under "action" (e.g., "single_arm", "gripper")
  • Language keys: Must match keys in meta/modality.json under "annotation" (e.g., "annotation.human.action.task_description")

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Optional fields

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sin_cos_embedding_keys (list[str] | None)

Specifies which state keys should use sine/cosine encoding. Best for dimensions that are in radians (e.g., joint angles). If not specified, min-max normalization is used. 
"state": ModalityConfig(
    delta_indices=[0],
    modality_keys=["single_arm", "gripper"],
    sin_cos_embedding_keys=["single_arm"],  # Apply sin/cos to joint angles
)
Sine/cosine embedding will duplicate the number of dimensions by 2, and is only recommended for proprioceptive states.

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mean_std_embedding_keys (list[str] | None)

Specifies which keys should use mean/standard deviation normalization instead of min-max normalization.

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action_configs (list[ActionConfig] | None)

Required for the "action" modality. Defines how each action modality should be interpreted and transformed. The list must have the same length as modality_keys.

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Configuring each modality

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Video modality

Defines which camera views to use: 
"video": ModalityConfig(
    delta_indices=[0],  # Current frame only
    modality_keys=[
        "front",  # Must match a key in meta/modality.json under "video"
    ],
)
For multiple cameras: 
"video": ModalityConfig(
    delta_indices=[0],
    modality_keys=["front", "wrist"],
)

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State modality

Defines proprioceptive observations (joint positions, gripper states, etc.): 
"state": ModalityConfig(
    delta_indices=[0],  # Current state
    modality_keys=[
        "single_arm",      # Must match keys in meta/modality.json under "state"
        "gripper",
    ],
)

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Action modality

Defines the action space and prediction horizon: 
"action": ModalityConfig(
    delta_indices=list(range(0, 16)),  # Predict 16 steps into the future
    modality_keys=[
        "single_arm",      # Must match keys in meta/modality.json under "action"
        "gripper",
    ],
    action_configs=[
        # One ActionConfig per modality_key
        # single_arm
        ActionConfig(
            rep=ActionRepresentation.RELATIVE,
            type=ActionType.NON_EEF,
            format=ActionFormat.DEFAULT,
        ),
        # gripper
        ActionConfig(
            rep=ActionRepresentation.ABSOLUTE,
            type=ActionType.NON_EEF,
            format=ActionFormat.DEFAULT,
        ),
    ],
)

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Language modality

Defines which language annotations to use: 
"language": ModalityConfig(
    delta_indices=[0],
    modality_keys=["annotation.human.action.task_description"],
)

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Understanding ActionConfig

Each ActionConfig has three required fields and one optional field.

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rep (ActionRepresentation)

Defines how actions should be interpreted:
  • RELATIVE: Actions are deltas from the current state (introduced in the UMI paper)
  • ABSOLUTE: Actions are target positions
Using relative actions will lead to smoother actions, but might suffer from drifting. If you want to use relative actions, make sure the state and action stored in the dataset are absolute, and the absolute to relative conversion will be handled in the processor.

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type (ActionType)

Specifies the control space:
  • EEF: End-effector/Cartesian space control (expecting a 9-dimensional vector: x, y, z positions + rotation 6D)
  • NON_EEF: Joint space control and other non-EEF control spaces (joint angles, positions, gripper positions, etc.)

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format (ActionFormat)

Defines the action representation format:
  • DEFAULT: Standard format (e.g., joint angles, gripper positions)
  • XYZ_ROT6D: 3D position + 6D rotation representation for end-effector control
  • XYZ_ROTVEC: 3D position + rotation vector for end-effector control

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state_key (str | None)

Optional. Specifies the corresponding reference state key for computing relative actions when rep=RELATIVE. If not provided, the system will use the action key as the reference state key. Example with state_key: 
"joint_pos_action_left": ActionConfig(
    rep=ActionRepresentation.RELATIVE,
    type=ActionType.NON_EEF,
    format=ActionFormat.DEFAULT,
    state_key="joint_pos_obs_left",  # Use this state to compute relative action
)

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Complete example: SO-100

Here’s the complete SO-100 configuration:
examples/SO100/so100_config.py
from gr00t.configs.data.embodiment_configs import register_modality_config
from gr00t.data.types import ModalityConfig, ActionConfig, ActionRepresentation, ActionType, ActionFormat
from gr00t.data.embodiment_tags import EmbodimentTag

so100_config = {
    "video": ModalityConfig(
        delta_indices=[0],
        modality_keys=["front", "wrist"],
    ),
    "state": ModalityConfig(
        delta_indices=[0],
        modality_keys=[
            "single_arm",
            "gripper",
        ],
    ),
    "action": ModalityConfig(
        delta_indices=list(range(0, 16)),
        modality_keys=[
            "single_arm",
            "gripper",
        ],
        action_configs=[
            ActionConfig(
                rep=ActionRepresentation.RELATIVE,
                type=ActionType.NON_EEF,
                format=ActionFormat.DEFAULT,
            ),
            ActionConfig(
                rep=ActionRepresentation.ABSOLUTE,
                type=ActionType.NON_EEF,
                format=ActionFormat.DEFAULT,
            ),
        ],
    ),
    "language": ModalityConfig(
        delta_indices=[0],
        modality_keys=["annotation.human.task_description"],
    ),
}

register_modality_config(so100_config, embodiment_tag=EmbodimentTag.NEW_EMBODIMENT)

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Key relationships with meta/modality.json

The modality configuration’s modality_keys must reference keys that exist in your dataset’s meta/modality.json. Example meta/modality.json: 
{
    "state": {
        "single_arm": {"start": 0, "end": 5},
        "gripper": {"start": 5, "end": 6}
    },
    "action": {
        "single_arm": {"start": 0, "end": 5},
        "gripper": {"start": 5, "end": 6}
    },
    "video": {
        "front": {"original_key": "observation.images.front"},
        "wrist": {"original_key": "observation.images.wrist"}
    },
    "annotation": {
        "human.task_description": {
            "original_key": "task_index"
        }
    }
}
The system will:
  1. Use modality_keys to look up the corresponding entries in meta/modality.json
  2. Extract the correct slices from the concatenated state/action arrays
  3. Apply the specified transformations (normalization, action representation conversion)

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Registering your configuration

After defining your configuration, register it so it’s available to the training and inference pipelines: 
from gr00t.configs.data.embodiment_configs import register_modality_config
from gr00t.data.embodiment_tags import EmbodimentTag

your_modality_config = {
    # ... your configuration
}

register_modality_config(your_modality_config, embodiment_tag=EmbodimentTag.NEW_EMBODIMENT)
Save your configuration to a Python file and pass the path to the --modality-config-path argument when running the fine-tuning script.

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