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# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import asdict, is_dataclass
from enum import Enum
from typing import Any
import numpy as np
from gr00t.configs.data.embodiment_configs import ModalityConfig
def apply_sin_cos_encoding(values: np.ndarray) -> np.ndarray:
"""Apply sin/cos encoding to values.
Args:
values: Array of shape (..., D) containing values to encode
Returns:
Array of shape (..., 2*D) with [sin, cos] concatenated
Note: This DOUBLES the dimension. For example:
Input: [v₁, v₂, v₃] with shape (..., 3)
Output: [sin(v₁), sin(v₂), sin(v₃), cos(v₁), cos(v₂), cos(v₃)] with shape (..., 6)
"""
sin_values = np.sin(values)
cos_values = np.cos(values)
# Concatenate sin and cos: [sin(v1), sin(v2), ..., cos(v1), cos(v2), ...]
return np.concatenate([sin_values, cos_values], axis=-1)
def nested_dict_to_numpy(data):
"""
Recursively converts bottom-level list of lists to NumPy arrays.
Args:
data: A nested dictionary where bottom nodes are list of lists,
and parent nodes are strings (keys)
Returns:
The same dictionary structure with bottom-level lists converted to NumPy arrays
Example:
>>> data = {"a": {"b": [[0, 1], [2, 3]]}}
>>> result = nested_dict_to_numpy(data)
>>> print(result["a"]["b"])
[[0 1]
[2 3]]
"""
if isinstance(data, dict):
return {key: nested_dict_to_numpy(value) for key, value in data.items()}
elif isinstance(data, list):
# Convert lists to numpy arrays
# NumPy will handle both 1D and 2D cases appropriately
return np.array(data)
else:
return data
def normalize_values_minmax(values, params):
"""
Normalize values using min-max normalization to [-1, 1] range.
Args:
values: Input values to normalize
- Shape: (T, D) or (B, T, D) where B is batch, T is time/step, D is feature dimension
- Can handle 2D or 3D arrays where last axis represents features
params: Dictionary with "min" and "max" keys
- params["min"]: Minimum values for normalization
* Case 1 - 1D bounds: Shape (D,) - same min/max for all steps
* Case 2 - 2D bounds: Shape (T, D) - different min/max per step
- params["max"]: Maximum values for normalization
* Case 1 - 1D bounds: Shape (D,) - same min/max for all steps
* Case 2 - 2D bounds: Shape (T, D) - different min/max per step
joint_group: Optional indexing for joint groups (legacy parameter)
Returns:
Normalized values in [-1, 1] range
- Same shape as input values: (T, D) or (B, T, D)
- Values are linearly mapped from [min, max] to [-1, 1]
- For features where min == max, normalized value is 0
Examples:
# 1D bounds - same normalization for all steps
values: (10, 5), params["min"]: (5,), params["max"]: (5,)
# 2D bounds - per-step normalization
values: (8, 4), params["min"]: (8, 4), params["max"]: (8, 4)
"""
min_vals = params["min"]
max_vals = params["max"]
normalized = np.zeros_like(values)
mask = ~np.isclose(max_vals, min_vals)
normalized[..., mask] = (values[..., mask] - min_vals[..., mask]) / (
max_vals[..., mask] - min_vals[..., mask]
)
normalized[..., mask] = 2 * normalized[..., mask] - 1
return normalized
def unnormalize_values_minmax(normalized_values, params):
"""
Min-max unnormalization from [-1, 1] range back to original range.
Args:
normalized_values: Normalized input values in [-1, 1] range
- Shape: (T, D) or (B, T, D) where B is batch, T is time/step, D is feature dimension
- Values outside [-1, 1] are automatically clipped
params: Dictionary with "min" and "max" keys
- params["min"]: Original minimum values used for normalization
* Case 1 - 1D bounds: Shape (D,) - same min/max for all steps
* Case 2 - 2D bounds: Shape (T, D) - different min/max per step
- params["max"]: Original maximum values used for normalization
* Case 1 - 1D bounds: Shape (D,) - same min/max for all steps
* Case 2 - 2D bounds: Shape (T, D) - different min/max per step
Returns:
Unnormalized values in original range [min, max]
- Same shape as input normalized_values: (T, D) or (B, T, D)
- Values are linearly mapped from [-1, 1] back to [min, max]
- Input values are clipped to [-1, 1] before unnormalization
Examples:
# 1D bounds - same unnormalization for all steps
normalized_values: (10, 5), params["min"]: (5,), params["max"]: (5,)
# 2D bounds - per-step unnormalization
normalized_values: (8, 4), params["min"]: (8, 4), params["max"]: (8, 4)
"""
min_vals = params["min"]
max_vals = params["max"]
range_vals = max_vals - min_vals
# Unnormalize from [-1, 1]
unnormalized = (np.clip(normalized_values, -1.0, 1.0) + 1.0) / 2.0 * range_vals + min_vals
return unnormalized
def normalize_values_meanstd(values, params):
"""
Normalize values using mean-std (z-score) normalization.
Args:
values: Input values to normalize
- Shape: (T, D) or (B, T, D) where B is batch, T is time/step, D is feature dimension
- Can handle 2D or 3D arrays where last axis represents features
params: Dictionary with "mean" and "std" keys
- params["mean"]: Mean values for normalization
* Case 1 - 1D params: Shape (D,) - same mean for all steps
* Case 2 - 2D params: Shape (T, D) - different mean per step
- params["std"]: Standard deviation values for normalization
* Case 1 - 1D params: Shape (D,) - same std for all steps
* Case 2 - 2D params: Shape (T, D) - different std per step
Returns:
Normalized values using z-score normalization
- Same shape as input values: (T, D) or (B, T, D)
- Values are transformed as: (x - mean) / std
- For features where std == 0, normalized value equals original value
Examples:
# 1D params - same normalization for all steps
values: (10, 5), params["mean"]: (5,), params["std"]: (5,)
# 2D params - per-step normalization
values: (8, 4), params["mean"]: (8, 4), params["std"]: (8, 4)
"""
mean_vals = params["mean"]
std_vals = params["std"]
# Create mask for non-zero standard deviations
mask = std_vals != 0
# Initialize normalized array
normalized = np.zeros_like(values)
# Normalize only features with non-zero std
normalized[..., mask] = (values[..., mask] - mean_vals[..., mask]) / std_vals[..., mask]
# Keep original values for zero-std features
normalized[..., ~mask] = values[..., ~mask]
return normalized
def unnormalize_values_meanstd(normalized_values, params):
"""
Mean-std unnormalization (reverse z-score normalization).
Args:
normalized_values: Normalized input values (z-scores)
- Shape: (T, D) or (B, T, D) where B is batch, T is time/step, D is feature dimension
- Can handle 2D or 3D arrays where last axis represents features
params: Dictionary with "mean" and "std" keys
- params["mean"]: Original mean values used for normalization
* Case 1 - 1D params: Shape (D,) - same mean for all steps
* Case 2 - 2D params: Shape (T, D) - different mean per step
- params["std"]: Original standard deviation values used for normalization
* Case 1 - 1D params: Shape (D,) - same std for all steps
* Case 2 - 2D params: Shape (T, D) - different std per step
Returns:
Unnormalized values in original scale
- Same shape as input normalized_values: (T, D) or (B, T, D)
- Values are transformed as: x * std + mean
- For features where std == 0, unnormalized value equals normalized value
Examples:
# 1D params - same unnormalization for all steps
normalized_values: (10, 5), params["mean"]: (5,), params["std"]: (5,)
# 2D params - per-step unnormalization
normalized_values: (8, 4), params["mean"]: (8, 4), params["std"]: (8, 4)
"""
mean_vals = params["mean"]
std_vals = params["std"]
# Create mask for non-zero standard deviations
mask = std_vals != 0
# Initialize unnormalized array
unnormalized = np.zeros_like(normalized_values)
# Unnormalize only features with non-zero std
unnormalized[..., mask] = (
normalized_values[..., mask] * std_vals[..., mask] + mean_vals[..., mask]
)
# Keep normalized values for zero-std features
unnormalized[..., ~mask] = normalized_values[..., ~mask]
return unnormalized
def to_json_serializable(obj: Any) -> Any:
"""
Recursively convert dataclasses and numpy arrays to JSON-serializable format.
Args:
obj: Object to convert (can be dataclass, numpy array, dict, list, etc.)
Returns:
JSON-serializable representation of the object
"""
if is_dataclass(obj) and not isinstance(obj, type):
# Convert dataclass to dict, then recursively process the dict
return to_json_serializable(asdict(obj))
elif isinstance(obj, np.ndarray):
# Convert numpy array to list
return obj.tolist()
elif isinstance(obj, np.integer):
# Convert numpy integers to Python int
return int(obj)
elif isinstance(obj, np.floating):
# Convert numpy floats to Python float
return float(obj)
elif isinstance(obj, np.bool_):
# Convert numpy bool to Python bool
return bool(obj)
elif isinstance(obj, dict):
# Recursively process dictionary values
return {key: to_json_serializable(value) for key, value in obj.items()}
elif isinstance(obj, (list, tuple)):
# Recursively process list/tuple elements
return [to_json_serializable(item) for item in obj]
elif isinstance(obj, set):
# Convert set to list
return [to_json_serializable(item) for item in obj]
elif isinstance(obj, (str, int, float, bool, type(None))):
# Already JSON-serializable
return obj
elif isinstance(obj, Enum):
return obj.name
else:
# For other types, try to convert to string as fallback
# You might want to handle specific types differently
return str(obj)
def parse_observation_gr00t(
obs: dict[str, Any], modality_configs: dict[str, Any]
) -> dict[str, Any]:
"""Reshape a flat ``{modality.key: value}`` observation into the nested,
batched ``{modality: {key: value}}`` form a GR00T policy expects.
Adds a leading batch dimension (``arr[None, :]``; strings become ``[[s]]``).
Shared by the eval, standalone-inference, and ONNX-export paths so they
cannot drift on modality set / key naming / batching.
"""
new_obs = {}
for modality in ["video", "state", "language"]:
new_obs[modality] = {}
for key in modality_configs[modality].modality_keys:
if modality == "language":
parsed_key = key
else:
parsed_key = f"{modality}.{key}"
arr = obs[parsed_key]
if isinstance(arr, str):
new_obs[modality][key] = [[arr]]
else:
new_obs[modality][key] = arr[None, :]
return new_obs
def parse_modality_configs(
modality_configs: dict[str, dict[str, ModalityConfig]],
) -> dict[str, dict[str, ModalityConfig]]:
parsed_modality_configs = {}
for embodiment_tag, modality_config in modality_configs.items():
parsed_modality_configs[embodiment_tag] = {}
for modality, config in modality_config.items():
if isinstance(config, dict):
parsed_modality_configs[embodiment_tag][modality] = ModalityConfig(**config)
else:
parsed_modality_configs[embodiment_tag][modality] = config
return parsed_modality_configs
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