r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by

Disable gradient checkpointing.

Handle client connection

Run a full pipeline: subtask generation followed by action prediction.

Convenience method for initializing all parameters, necessary due to the quirks of linen.

Convenience method for initializing all parameters, necessary due to the quirks of linen.

Check if gradient checkpointing is enabled.

Listen for periodic refresh messages from internal queue.

Load the normalization stats from a directory.

Load JAX model and optionally convert to PyTorch. Args: checkpoint_dir: Path to the JAX checkpoint directory output_path: Path to

Creates a random input example for the Aloha policy.

Creates a random input example for the ARX policy.

Make a boolean mask for the given dimensions. Example: make_bool_mask(2, -2, 2) == (True, True, False, False, True, True) make_bo

Creates a random input example for the Droid policy.

生成符合当前LeRobot数据集格式的示例数据,匹配转换代码输出

生成符合 Subtask LeRobot 数据集格式的示例数据

Creates a random input example for the Libero policy.

generate example data for Libero Subtask dataset

Download a file or directory from a remote filesystem to the local cache, and return the local path. If the local file already exists, it will be

The model type.

A higher-order function to JIT-compile `nnx.Module` methods, freezing the module's state in the process. Why not `nnx.jit`? For some reason, naiv

自定义观察回调函数

Preprocess the observations by performing image augmentations (if train=True), resizing (if necessary), and filling in a default image mask (if ne

Torch.compile-compatible version of preprocess_observation_pytorch with simplified type annotations. This function avoids complex type annotation

Reset the policy to its initial state.

Reset the environment to its initial state. This will be called once before starting each episode.

Replicates tf.image.resize_with_pad for multiple images using PIL. Resizes a batch of images to a target height. Args: images: A batch of

Replicates tf.image.resize_with_pad. Resizes an image to a target height and width without distortion by padding with black. If the image is float

PyTorch version of resize_with_pad. Resizes an image to a target height and width without distortion by padding with black. If the image is float3

Restores unstructured params PyTree from a checkpoint. This works with checkpoints saved with `save_state` during openpi training (see `training/

Reformat observation and action keys, sample language instruction.

Runs the runtime loop in a new thread.

Do a full inference forward and compute the action (batch_size x num_steps x num_motors)