Determines the location of assets (e.g., norm stats) that will be used to set up the data pipeline. These assets will be replicated inside the ch

This config is used to configure transforms that are applied at various parts of the data pipeline. For your own dataset, you can copy this c

Asynchronous Pi0.5 inference engine， support subtask generation and action prediction.

Load a policy from a trained checkpoint.

LeRobot 数据格式的异步推理观察器

Data config for Libero environment with subtask support. Assumes features stored as: - images.agentview_rgb: image (3, 256, 256) uint8

Creates model transforms for standard pi0 models.

Synchronous Pi0.5 inference client

Asynchronous Pi0.5 websocket client with request-id routing.

Truly asynchronous Pi0.5 inference WebSocket server for LIBERO.

Transformer MLP / feed-forward block.

Config for training on DROID, using RLDS data format (for efficient training on larger datasets).

Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`SiglipEncoderLayer`]. Args:

Synchronous Pi0.5 inference engine

Adds learned positional embeddings to the inputs. Attributes: posemb_init: positional embedding initializer.

Attention module.

Attention module.

Attention module.

A CheckpointHandler for calling an arbitrary function asynchronously. Only for saving, not for restoring.

A composite transform that applies a sequence of transforms in order.

Embedder module.

Embedder module.

Embedder module.

Transformer Model Encoder for sequence to sequence translation.

Single transformer encoder block (MHSA + MLP).

Gated Linear Unit with GELU (GeGLU) activation function. GeGLU is a Flax layer that combines a linear transformation with a GELU activation fu

Multi-headed attention from 'Attention Is All You Need' paper

A group of transforms.

Identity layer, convenient for giving a name to an array.

Example data config for custom DROID dataset in LeRobot format. To convert your custom DROID dataset (<10s of hours) to LeRobot format, see e

Multihead Attention Pooling.

Transformer MLP / feed-forward block.

Holds observations, i.e., inputs to the model. See `Observation.from_dict` to see the expected dictionary form. This is the format that shoul

r""" loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token pr

r""" past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):

Shallow wrapper around the DROID data loader to make it compatible with openpi. All batching already happens in the DROID dataset, so we don't ne

Environment for real robot bi-manual manipulation Action space: [left_arm_qpos (6), # absolute joint position

Multi-headed attention from 'Attention Is All You Need' paper

Multihead Attention Pooling.

r""" loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text simila

Creates model transforms for subtask-based hierarchical learning.

Simple synchronous JSON websocket client for Pi0.5 server. Each request sends images, high-level prompt, and state. Server generates subt

Synchronous Pi0.5 inference with optional subtask generation.

Synchronous Pi0.5 inference server (JSON over websockets).

Pi0.5 inference server based on WebSocket. Simplified synchronous approach: - Each request generates subtask first, then actions - Re

Records timing measurements for different keys.

Torch data loader implementation.

ViT model.

Repacks delta actions into absolute action space.

Wraps a policy to return action chunks one-at-a-time. Assumes that the first dimension of all action fields is the chunk size. A new inferen

AdamW optimizer.

An Agent is the thing with agency, i.e. the entity that makes decisions. Agents receive observations about the state of the world, and return act