"""See _CONFIGS for the list of available configs.""" import abc import json import os from collections.abc import Sequence import dataclasses import difflib import logging import pathlib from typing import Any, Protocol, TypeAlias import etils.epath as epath import flax.nnx as nnx from typing_extensions import override import tyro import openpi.models.model as _model import openpi.models.pi0 as pi0 import openpi.models.pi0_fast as pi0_fast import openpi.models.tokenizer as _tokenizer import openpi.policies.aloha_policy as aloha_policy import openpi.policies.droid_policy as droid_policy import openpi.policies.libero_policy as libero_policy import openpi.policies.robocasa_policy as robocasa_policy import openpi.shared.download as _download import openpi.shared.normalize as _normalize import openpi.training.droid_rlds_dataset as droid_rlds_dataset import openpi.training.optimizer as _optimizer import openpi.training.weight_loaders as weight_loaders import openpi.transforms as _transforms import numpy as np import openpi.groot_utils.groot_openpi_dataset as _groot_openpi_dataset from robocasa.macros import DATASET_BASE_PATH from robocasa.utils.dataset_registry import DATASET_SOUP_REGISTRY from robocasa.utils.dataset_registry_utils import get_ds_meta ModelType: TypeAlias = _model.ModelType # Work around a tyro issue with using nnx.filterlib.Filter directly. Filter: TypeAlias = nnx.filterlib.Filter @dataclasses.dataclass(frozen=True) class AssetsConfig: """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 checkpoint under the `assets/asset_id` directory. This can be used to load assets from a different checkpoint (e.g., base model checkpoint) or some other centralized location. For example, to load the norm stats for the Trossen robot from the base model checkpoint during fine-tuning, use: ``` AssetsConfig( assets_dir="gs://openpi-assets/checkpoints/pi0_base/assets", asset_id="trossen", ) ``` """ # Assets directory. If not provided, the config assets_dirs will be used. This is useful to load assets from # a different checkpoint (e.g., base model checkpoint) or some other centralized location. assets_dir: str | None = None # Asset id. If not provided, the repo id will be used. This allows users to reference assets that describe # different robot platforms. asset_id: str | None = None @dataclasses.dataclass(frozen=True) class DataConfig: # LeRobot repo id. If None, fake data will be created. repo_id: str | None = None # Directory within the assets directory containing the data assets. asset_id: str | None = None # Contains precomputed normalization stats. If None, normalization will not be performed. norm_stats: dict[str, _transforms.NormStats] | None = None # Used to adopt the inputs from a dataset specific format to a common format # which is expected by the data transforms. repack_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group) # Data transforms, typically include robot specific transformations. Will be applied # before the data is normalized. See `model.Observation` and `model.Actions` to learn about the # normalized data. data_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group) # Model specific transforms. Will be applied after the data is normalized. model_transforms: _transforms.Group = dataclasses.field(default_factory=_transforms.Group) # If true, will use quantile normalization. Otherwise, normal z-score normalization will be used. use_quantile_norm: bool = False # Names of keys that will be used by the data loader to generate the action sequence. The length of the # sequence is defined by the `action_horizon` field in the model config. This should be adjusted if your # LeRobot dataset is using different keys to represent the action. action_sequence_keys: Sequence[str] = ("actions",) # If true, will use the LeRobot dataset task to define the prompt. prompt_from_task: bool = False # Only used for RLDS data loader (ie currently only used for DROID). rlds_data_dir: str | None = None # Action space for DROID dataset. action_space: droid_rlds_dataset.DroidActionSpace | None = None # Action dimension for padding (used by Groot datasets) action_dim: int | None = None # Multi-dataset support for Groot datasets data_dirs: list[str] | None = None # List of data directories for multi-dataset dataset_weights: list[float] | None = None # Weights for each dataset in multi-dataset class GroupFactory(Protocol): def __call__(self, model_config: _model.BaseModelConfig) -> _transforms.Group: """Create a group.""" @dataclasses.dataclass(frozen=True) class ModelTransformFactory(GroupFactory): """Creates model transforms for standard pi0 models.""" # If provided, will determine the default prompt that be used by the model. default_prompt: str | None = None def __call__(self, model_config: _model.BaseModelConfig) -> _transforms.Group: match model_config.model_type: case _model.ModelType.PI0: return _transforms.Group( inputs=[ _transforms.InjectDefaultPrompt(self.default_prompt), _transforms.ResizeImages(224, 224), _transforms.TokenizePrompt( _tokenizer.PaligemmaTokenizer(model_config.max_token_len), ), ], ) case _model.ModelType.PI05: assert isinstance(model_config, pi0.Pi0Config) return _transforms.Group( inputs=[ _transforms.InjectDefaultPrompt(self.default_prompt), _transforms.ResizeImages(224, 224), _transforms.TokenizePrompt( _tokenizer.PaligemmaTokenizer(model_config.max_token_len), discrete_state_input=model_config.discrete_state_input, ), _transforms.PadStatesAndActions(model_config.action_dim), ], ) case _model.ModelType.PI0_FAST: return _transforms.Group( inputs=[ _transforms.InjectDefaultPrompt(self.default_prompt), _transforms.ResizeImages(224, 224), _transforms.TokenizeFASTInputs( _tokenizer.FASTTokenizer(model_config.max_token_len), ), ], outputs=[ _transforms.ExtractFASTActions( _tokenizer.FASTTokenizer(model_config.max_token_len), action_horizon=model_config.action_horizon, action_dim=model_config.action_dim, ) ], ) @dataclasses.dataclass(frozen=True) class DataConfigFactory(abc.ABC): # The LeRobot repo id. repo_id: str | None = None # Determines how the assets will be loaded. assets: AssetsConfig = dataclasses.field(default_factory=AssetsConfig) # Base config that will be updated by the factory. base_config: tyro.conf.Suppress[DataConfig | None] = None @abc.abstractmethod def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: """Create a data config.""" def create_base_config(self, assets_dirs: pathlib.Path) -> DataConfig: repo_id = self.repo_id if self.repo_id is not tyro.MISSING else None asset_id = self.assets.asset_id or repo_id base = self.base_config or DataConfig() # Preserve pre-supplied norm_stats; only load if not provided existing_stats = base.norm_stats loaded_stats = None if existing_stats is not None else self._load_norm_stats( epath.Path(self.assets.assets_dir or assets_dirs), asset_id ) return dataclasses.replace( base, repo_id=repo_id, asset_id=asset_id, norm_stats=existing_stats if existing_stats is not None else loaded_stats, ) def _load_norm_stats(self, assets_dir: epath.Path, asset_id: str | None) -> dict[str, _transforms.NormStats] | None: if asset_id is None: return None try: data_assets_dir = str(assets_dir / asset_id) norm_stats = _normalize.load(_download.maybe_download(data_assets_dir)) logging.info(f"Loaded norm stats from {data_assets_dir}") return norm_stats except FileNotFoundError: logging.info(f"Norm stats not found in {data_assets_dir}.") # Fallback: try to read and convert stats from repo meta # TODO: fix converted = _groot_openpi_dataset._convert_stats_from_repo_meta(asset_id) if converted is not None: logging.info(f"Converted norm stats from repo meta for {asset_id}") return converted return None @dataclasses.dataclass(frozen=True) class FakeDataConfig(DataConfigFactory): repo_id: str = "fake" @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: return DataConfig(repo_id=self.repo_id) @dataclasses.dataclass(frozen=True) class SimpleDataConfig(DataConfigFactory): # Factory for the data transforms. data_transforms: tyro.conf.Suppress[GroupFactory] = dataclasses.field(default_factory=GroupFactory) # Factory for the model transforms. model_transforms: tyro.conf.Suppress[GroupFactory] = dataclasses.field(default_factory=ModelTransformFactory) @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: return dataclasses.replace( self.create_base_config(assets_dirs), data_transforms=self.data_transforms(model_config), model_transforms=self.model_transforms(model_config), use_quantile_norm=model_config.model_type in (ModelType.PI0_FAST, ModelType.PI05), ) @dataclasses.dataclass(frozen=True) class LeRobotAlohaDataConfig(DataConfigFactory): # If true, will convert joint dimensions to deltas with respect to the current state before passing to the model. # Gripper dimensions will remain in absolute values. use_delta_joint_actions: bool = True # If provided, will be injected into the input data if the "prompt" key is not present. default_prompt: str | None = None # If true, this will convert the joint and gripper values from the standard Aloha space to # the space used by the pi internal runtime which was used to train the base model. People who # use standard Aloha data should set this to true. adapt_to_pi: bool = True # Repack transforms. repack_transforms: tyro.conf.Suppress[_transforms.Group] = dataclasses.field( default=_transforms.Group( inputs=[ _transforms.RepackTransform( { "images": {"cam_high": "observation.images.top"}, "state": "observation.state", "actions": "action", } ) ] ) ) # Action keys that will be used to read the action sequence from the dataset. action_sequence_keys: Sequence[str] = ("action",) @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: data_transforms = _transforms.Group( inputs=[aloha_policy.AlohaInputs(action_dim=model_config.action_dim, adapt_to_pi=self.adapt_to_pi)], outputs=[aloha_policy.AlohaOutputs(adapt_to_pi=self.adapt_to_pi)], ) if self.use_delta_joint_actions: delta_action_mask = _transforms.make_bool_mask(6, -1, 6, -1) data_transforms = data_transforms.push( inputs=[_transforms.DeltaActions(delta_action_mask)], outputs=[_transforms.AbsoluteActions(delta_action_mask)], ) model_transforms = ModelTransformFactory(default_prompt=self.default_prompt)(model_config) return dataclasses.replace( self.create_base_config(assets_dirs), repack_transforms=self.repack_transforms, data_transforms=data_transforms, model_transforms=model_transforms, action_sequence_keys=self.action_sequence_keys, ) @dataclasses.dataclass(frozen=True) class LeRobotLiberoDataConfig(DataConfigFactory): """ 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 class and modify the transforms to match your dataset based on the comments below. """ @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: # The repack transform is *only* applied to the data coming from the dataset, # and *not* during inference. We can use it to make inputs from the dataset look # as close as possible to those coming from the inference environment (e.g. match the keys). # Below, we match the keys in the dataset (which we defined in the data conversion script) to # the keys we use in our inference pipeline (defined in the inference script for libero). # For your own dataset, first figure out what keys your environment passes to the policy server # and then modify the mappings below so your dataset's keys get matched to those target keys. # The repack transform simply remaps key names here. repack_transform = _transforms.Group( inputs=[ _transforms.RepackTransform( { "observation/image": "image", "observation/wrist_image": "wrist_image", "observation/state": "state", "actions": "actions", "prompt": "prompt", } ) ] ) # The data transforms are applied to the data coming from the dataset *and* during inference. # Below, we define the transforms for data going into the model (``inputs``) and the transforms # for data coming out of the model (``outputs``) (the latter is only used during inference). # We defined these transforms in `libero_policy.py`. You can check the detailed comments there for # how to modify the transforms to match your dataset. Once you created your own transforms, you can # replace the transforms below with your own. data_transforms = _transforms.Group( inputs=[libero_policy.LiberoInputs(action_dim=model_config.action_dim, model_type=model_config.model_type)], outputs=[libero_policy.LiberoOutputs()], ) # One additional data transform: pi0 models are trained on delta actions (relative to the first # state in each action chunk). IF your data has ``absolute`` actions (e.g. target joint angles) # you can uncomment the following line to convert the actions to delta actions. The only exception # is for the gripper actions which are always absolute. # In the example below, we would apply the delta conversion to the first 6 actions (joints) and # leave the 7th action (gripper) unchanged, i.e. absolute. # In Libero, the raw actions in the dataset are already delta actions, so we *do not* need to # apply a separate delta conversion (that's why it's commented out). Choose whether to apply this # transform based on whether your dataset uses ``absolute`` or ``delta`` actions out of the box. # TODO(karl): comment this out once we have updated the Libero checkpoints to not use # the delta action transform delta_action_mask = _transforms.make_bool_mask(6, -1) data_transforms = data_transforms.push( inputs=[_transforms.DeltaActions(delta_action_mask)], outputs=[_transforms.AbsoluteActions(delta_action_mask)], ) # Model transforms include things like tokenizing the prompt and action targets # You do not need to change anything here for your own dataset. model_transforms = ModelTransformFactory()(model_config) # We return all data transforms for training and inference. No need to change anything here. return dataclasses.replace( self.create_base_config(assets_dirs), repack_transforms=repack_transform, data_transforms=data_transforms, model_transforms=model_transforms, ) @dataclasses.dataclass(frozen=True) class RLDSDroidDataConfig(DataConfigFactory): """ Config for training on DROID, using RLDS data format (for efficient training on larger datasets). """ rlds_data_dir: str | None = None action_space: droid_rlds_dataset.DroidActionSpace | None = None @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: repack_transform = _transforms.Group( inputs=[ _transforms.RepackTransform( { "observation/exterior_image_1_left": "observation/image", "observation/wrist_image_left": "observation/wrist_image", "observation/joint_position": "observation/joint_position", "observation/gripper_position": "observation/gripper_position", "actions": "actions", "prompt": "prompt", } ) ] ) data_transforms = _transforms.Group( inputs=[droid_policy.DroidInputs(action_dim=model_config.action_dim, model_type=model_config.model_type)], outputs=[droid_policy.DroidOutputs()], ) if self.action_space == droid_rlds_dataset.DroidActionSpace.JOINT_POSITION: # Data loader returns absolute joint position actions -- convert to delta actions for training. delta_action_mask = _transforms.make_bool_mask(7, -1) data_transforms = data_transforms.push( inputs=[_transforms.DeltaActions(delta_action_mask)], outputs=[_transforms.AbsoluteActions(delta_action_mask)], ) model_transforms = ModelTransformFactory()(model_config) assert self.rlds_data_dir is not None, "Need to set rlds data dir for RLDS data loader." return dataclasses.replace( self.create_base_config(assets_dirs), repack_transforms=repack_transform, data_transforms=data_transforms, model_transforms=model_transforms, use_quantile_norm=model_config.model_type in (ModelType.PI0_FAST, ModelType.PI05), rlds_data_dir=self.rlds_data_dir, action_space=self.action_space, ) @dataclasses.dataclass(frozen=True) class LeRobotRobocasaDataConfig(DataConfigFactory): """Config for training on Groot datasets. Set `assets.asset_id` (inherited from DataConfigFactory) to a directory under `assets_dirs/` that holds the precomputed `norm_stats.json` (mean/std/q01/q99) for this RoboCasa mixture — produced by `scripts/compute_norm_stats_robocasa.py`. """ repo_id: str | None = None data_dirs: Any | None = None dataset_weights: list[float] | None = None action_dim: int | None = None # REPRO (2026-09-22): the ctc502 60k BASE checkpoint was trained upstream with plain # z-score (mean/std) normalisation -- upstream's config.py never assigns # use_quantile_norm anywhere, so it kept its `False` default, and the checkpoint's own # assets/norm_stats.json has q01/q99 = null accordingly. Serving those weights under # quantile norm applies a different transform than they were trained with. Set this on # an eval-only config for that lineage. Leave False for everything trained in THIS repo # (negmesh / mimicgen arms), which really is quantile-normalised under ctc502_qnorm. force_zscore_norm: bool = False @override def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig: repack_transform = _transforms.Group() data_transforms = _transforms.Group( inputs=[robocasa_policy.RobocasaInputs(action_dim=model_config.action_dim, model_type=model_config.model_type)], outputs=[robocasa_policy.RobocasaOutputs()], ) model_transforms = ModelTransformFactory()(model_config) base = self.create_base_config(assets_dirs) # Fallback: if norm_stats are not available in assets, derive a quick approximation # from per-task stats.json files. We try this best-effort because eval-only nodes may # not have the dataset on disk; at inference time `create_trained_policy` will then # read norm_stats directly from the checkpoint's assets/. # NOTE: this fallback only fills mean/std (no q01/q99). For pi0-FAST training, prefer # running `scripts/compute_norm_stats_robocasa.py` so quantiles are accurate. fallback_norm_stats = None if base.norm_stats is None and self.data_dirs and len(self.data_dirs) > 0: try: if len(self.data_dirs) == 1: d = self.data_dirs[0] norm_stats = _groot_openpi_dataset._load_norm_stats_from_groot_dataset(d) if norm_stats is not None: fallback_norm_stats = norm_stats logging.info(f"Loaded norm stats from local data dir: {d}") else: norm_stats = _groot_openpi_dataset._load_norm_stats_from_groot_mixture_dataset(self.data_dirs) if norm_stats is not None: fallback_norm_stats = norm_stats logging.info(f"Loaded combined norm stats from {len(self.data_dirs)} data dirs") except FileNotFoundError as e: logging.warning( "Could not load norm stats from data_dirs (%s); will fall back to checkpoint assets at inference time.", e, ) return dataclasses.replace( base, repack_transforms=repack_transform, data_transforms=data_transforms, model_transforms=model_transforms, action_dim=model_config.action_dim, data_dirs=self.data_dirs, dataset_weights=self.dataset_weights, norm_stats=base.norm_stats or fallback_norm_stats, # pi0-FAST uses FAST tokenization which assumes inputs in [-1, 1]; switch to # quantile normalization (q01/q99 -> [-1, 1]) for that model. For pi0 (non-FAST) # we keep the default z-score normalization. use_quantile_norm=(not self.force_zscore_norm) and model_config.model_type in (_model.ModelType.PI0_FAST, _model.ModelType.PI05), ) @dataclasses.dataclass class TrainConfig: # Name of the config. Must be unique. Will be used to reference this config. name: tyro.conf.Suppress[str] # Project name. project_name: str = "openpi" # Experiment name. Will be used to name the metadata and checkpoint directories. exp_name: str = tyro.MISSING # Defines the model config. Some attributes (action_dim, action_horizon, and max_token_len) are shared by all models # -- see BaseModelConfig. Specific model implementations (e.g., Pi0Config) inherit from BaseModelConfig and may # define additional attributes. model: _model.BaseModelConfig = dataclasses.field(default_factory=pi0.Pi0Config) # A weight loader can optionally load (possibly partial) weights from disk after the model is initialized. weight_loader: weight_loaders.WeightLoader = dataclasses.field(default_factory=weight_loaders.NoOpWeightLoader) lr_schedule: _optimizer.LRScheduleConfig = dataclasses.field(default_factory=_optimizer.CosineDecaySchedule) optimizer: _optimizer.OptimizerConfig = dataclasses.field(default_factory=_optimizer.AdamW) ema_decay: float | None = 0.99 # Specifies which weights should be frozen. freeze_filter: tyro.conf.Suppress[Filter] = dataclasses.field(default_factory=nnx.Nothing) # Determines the data to be trained on. data: DataConfigFactory = dataclasses.field(default_factory=FakeDataConfig) # Base directory for config assets (e.g., norm stats). assets_base_dir: str = "./assets" # Base directory for checkpoints. checkpoint_base_dir: str = "./checkpoints" # Random seed that will be used by random generators during training. seed: int = 42 # Global batch size. batch_size: int = 32 # Number of workers to use for the data loader. Increasing this number will speed up data loading but # will increase memory and CPU usage. num_workers: int = 2 # Number of train steps (batches) to run. num_train_steps: int = 30_000 # How often (in steps) to log training metrics. log_interval: int = 100 # How often (in steps) to save checkpoints. save_interval: int = 1000 # If set, checkpoints are saved at EXACTLY these steps and save_interval is ignored. # save_interval can only express a regular cadence; an irregular schedule needs a list. # NOTE: checkpoints.py hardcodes max_to_keep=1, so keep_period is what actually PRESERVES # a saved step. Any step listed here must be divisible by keep_period or it will be # pruned as soon as the next checkpoint lands. save_steps: tuple[int, ...] | None = None # If set, any existing checkpoints matching step % keep_period == 0 will not be deleted. keep_period: int | None = 5000 # If true, will overwrite the checkpoint directory if it already exists. overwrite: bool = False # If true, will resume training from the last checkpoint. resume: bool = False # If true, will enable wandb logging. wandb_enabled: bool = True # Used to pass metadata to the policy server. policy_metadata: dict[str, Any] | None = None # If the value is greater than 1, FSDP will be enabled and shard across number of specified devices; overall # device memory will be reduced but training could potentially be slower. # eg. if total device is 4 and fsdp devices is 2; then the model will shard to 2 devices and run # data parallel between 2 groups of devices. fsdp_devices: int = 1 @property def assets_dirs(self) -> pathlib.Path: """Get the assets directory for this config.""" return (pathlib.Path(self.assets_base_dir) / self.name).resolve() @property def checkpoint_dir(self) -> pathlib.Path: """Get the checkpoint directory for this config.""" if not self.exp_name: raise ValueError("--exp_name must be set") return (pathlib.Path(self.checkpoint_base_dir) / self.name / self.exp_name).resolve() @property def trainable_filter(self) -> nnx.filterlib.Filter: """Get the filter for the trainable parameters.""" return nnx.All(nnx.Param, nnx.Not(self.freeze_filter)) def __post_init__(self) -> None: if self.resume and self.overwrite: raise ValueError("Cannot resume and overwrite at the same time.") # Use `get_config` if you need to get a config by name in your code. _CONFIGS = [ # # Inference Aloha configs. # TrainConfig( name="pi0_aloha", model=pi0.Pi0Config(), data=LeRobotAlohaDataConfig( assets=AssetsConfig(asset_id="trossen"), ), policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]}, ), TrainConfig( name="pi0_aloha_towel", model=pi0.Pi0Config(), data=LeRobotAlohaDataConfig( assets=AssetsConfig(asset_id="trossen"), default_prompt="fold the towel", ), policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]}, ), TrainConfig( name="pi0_aloha_tupperware", model=pi0.Pi0Config(), data=LeRobotAlohaDataConfig( assets=AssetsConfig(asset_id="trossen"), default_prompt="open the tupperware and put the food on the plate", ), policy_metadata={"reset_pose": [0, -1.5, 1.5, 0, 0, 0]}, ), # # Inference DROID configs. # TrainConfig( name="pi0_droid", model=pi0.Pi0Config(action_horizon=10), data=SimpleDataConfig( assets=AssetsConfig(asset_id="droid"), data_transforms=lambda model: _transforms.Group( inputs=[droid_policy.DroidInputs(action_dim=model.action_dim)], outputs=[droid_policy.DroidOutputs()], ), base_config=DataConfig( prompt_from_task=True, ), ), ), TrainConfig( name="pi0_fast_droid", model=pi0_fast.Pi0FASTConfig(action_dim=8, action_horizon=10), data=SimpleDataConfig( assets=AssetsConfig(asset_id="droid"), data_transforms=lambda model: _transforms.Group( inputs=[droid_policy.DroidInputs(action_dim=model.action_dim, model_type=ModelType.PI0_FAST)], outputs=[droid_policy.DroidOutputs()], ), base_config=DataConfig( prompt_from_task=True, ), ), ), # # Fine-tuning Libero configs. # # These train configs define the hyperparameters for fine-tuning the base model on your own dataset. # They are used to define key elements like the dataset you are training on, the base checkpoint you # are using, and other hyperparameters like how many training steps to run or what learning rate to use. # For your own dataset, you can copy this class and modify the dataset name, and data transforms based on # the comments below. TrainConfig( # Change the name to reflect your model and dataset. name="pi0_libero", # Here you define the model config -- In this example we use pi0 as the model # architecture and perform *full* finetuning. in the examples below we show how to modify # this to perform *low-memory* (LORA) finetuning and use pi0-FAST as an alternative architecture. model=pi0.Pi0Config(), # Here you define the dataset you are training on. In this example we use the Libero # dataset. For your own dataset, you can change the repo_id to point to your dataset. # Also modify the DataConfig to use the new config you made for your dataset above. data=LeRobotLiberoDataConfig( repo_id="physical-intelligence/libero", base_config=DataConfig( # This flag determines whether we load the prompt (i.e. the task instruction) from the # ``task`` field in the LeRobot dataset. If set to True, the prompt will show up in # a field called ``prompt`` in the input dict. The recommended setting is True. prompt_from_task=True, ), ), # Here you define which pre-trained checkpoint you want to load to initialize the model. # This should match the model config you chose above -- i.e. in this case we use the pi0 base model. weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), # Below you can define other hyperparameters like the learning rate, number of training steps, etc. # Check the base TrainConfig class for a full list of available hyperparameters. num_train_steps=30_000, ), TrainConfig( name="pi0_libero_low_mem_finetune", # Here is an example of loading a pi0 model for LoRA fine-tuning. model=pi0.Pi0Config(paligemma_variant="gemma_2b_lora", action_expert_variant="gemma_300m_lora"), data=LeRobotLiberoDataConfig( repo_id="physical-intelligence/libero", base_config=DataConfig(prompt_from_task=True), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=30_000, # The freeze filter defines which parameters should be frozen during training. # We have a convenience function in the model config that returns the default freeze filter # for the given model config for LoRA finetuning. Just make sure it matches the model config # you chose above. freeze_filter=pi0.Pi0Config( paligemma_variant="gemma_2b_lora", action_expert_variant="gemma_300m_lora" ).get_freeze_filter(), # Turn off EMA for LoRA finetuning. ema_decay=None, ), TrainConfig( name="pi0_fast_libero", # Here is an example of loading a pi0-FAST model for full finetuning. # Modify action_dim and action_horizon to match your dataset (action horizon is equal to # the desired action chunk length). # The max_token_len is the maximum number of (non-image) tokens the model can handle. # This includes the tokenized prompt, proprioceptive state, and (FAST-tokenized) action tokens. # Choosing this value too small may chop off tokens at the end of your sequence (the code will throw # a warning), while choosing it too large will waste memory (since we pad each batch element to the # max_token_len). A good rule of thumb is to use approx 180 for single-arm robots, and approx 250 for # two-arm robots. Generally, err on the lower side here first, and potentially increase the value if # you see many warnings being thrown during training. model=pi0_fast.Pi0FASTConfig(action_dim=7, action_horizon=10, max_token_len=180), data=LeRobotLiberoDataConfig( repo_id="physical-intelligence/libero", base_config=DataConfig(prompt_from_task=True), ), # Note that we load the pi0-FAST base model checkpoint here. weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"), num_train_steps=30_000, ), TrainConfig( name="pi0_fast_libero_low_mem_finetune", # Here is an example of loading a pi0-FAST model for LoRA finetuning. # For setting action_dim, action_horizon, and max_token_len, see the comments above. model=pi0_fast.Pi0FASTConfig( action_dim=7, action_horizon=10, max_token_len=180, paligemma_variant="gemma_2b_lora" ), data=LeRobotLiberoDataConfig( repo_id="physical-intelligence/libero", base_config=DataConfig(prompt_from_task=True), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"), num_train_steps=30_000, # Again, make sure to match the model config above when extracting the freeze filter # that specifies which parameters should be frozen during LoRA finetuning. freeze_filter=pi0_fast.Pi0FASTConfig( action_dim=7, action_horizon=10, max_token_len=180, paligemma_variant="gemma_2b_lora" ).get_freeze_filter(), # Turn off EMA for LoRA finetuning. ema_decay=None, ), # # Fine-tuning Aloha configs. # # This is a test config that is used to illustate how train on a custom LeRobot dataset. # For instuctions on how to convert and train on your own Aloha dataset see examples/aloha_real/README.md TrainConfig( name="pi0_aloha_pen_uncap", model=pi0.Pi0Config(), data=LeRobotAlohaDataConfig( repo_id="physical-intelligence/aloha_pen_uncap_diverse", assets=AssetsConfig( assets_dir="gs://openpi-assets/checkpoints/pi0_base/assets", asset_id="trossen", ), default_prompt="uncap the pen", repack_transforms=_transforms.Group( inputs=[ _transforms.RepackTransform( { "images": { "cam_high": "observation.images.cam_high", "cam_left_wrist": "observation.images.cam_left_wrist", "cam_right_wrist": "observation.images.cam_right_wrist", }, "state": "observation.state", "actions": "action", } ) ] ), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=20_000, ), # # Fine-tuning DROID configs. # TrainConfig( name="pi0_fast_droid_finetune", model=pi0_fast.Pi0FASTConfig( action_dim=8, action_horizon=16, max_token_len=180, ), data=RLDSDroidDataConfig( repo_id="droid", # Set this to the path to your DROID RLDS dataset (the parent directory of the `droid` directory). rlds_data_dir="", action_space=droid_rlds_dataset.DroidActionSpace.JOINT_POSITION, ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=5e-5, decay_steps=1_000_000, decay_lr=5e-5, ), num_train_steps=100_000, # 100k steps should be sufficient, takes ~2 days on 8x H100s batch_size=256, log_interval=100, save_interval=5000, keep_period=20_000, num_workers=0, # Important: RLDS DataLoader requires num_workers=0, handles multi-processing internally ), # # ALOHA Sim configs. This config is used to demonstrate how to train on a simple simulated environment. # TrainConfig( name="pi0_aloha_sim", model=pi0.Pi0Config(), data=LeRobotAlohaDataConfig( repo_id="lerobot/aloha_sim_transfer_cube_human", default_prompt="Transfer cube", use_delta_joint_actions=False, ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=20_000, ), # # Debugging configs. # TrainConfig( name="debug", data=FakeDataConfig(), batch_size=2, model=pi0.Pi0Config(paligemma_variant="dummy", action_expert_variant="dummy"), save_interval=100, overwrite=True, exp_name="debug", num_train_steps=10, wandb_enabled=False, ), TrainConfig( name="debug_restore", data=FakeDataConfig(), batch_size=2, model=pi0.Pi0Config(paligemma_variant="dummy", action_expert_variant="dummy"), weight_loader=weight_loaders.CheckpointWeightLoader("./checkpoints/debug/debug/9/params"), overwrite=True, exp_name="debug", num_train_steps=10, wandb_enabled=False, ), # # RoboCasa dataset configs. # TrainConfig( name="pi0_robocasa_target50", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target50"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=500000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_finetune_target_atomic_seen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_atomic_seen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("INSERT_CKPTPOINT_HERE"), num_train_steps=100000, save_interval=5000, keep_period=5000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_finetune_target_composite_seen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_composite_seen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("INSERT_CKPTPOINT_HERE"), num_train_steps=100000, save_interval=5000, keep_period=5000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_finetune_target_composite_unseen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_composite_unseen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("INSERT_CKPTPOINT_HERE"), num_train_steps=100000, save_interval=5000, keep_period=5000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_target_atomic_seen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_atomic_seen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_target_atomic_seen_random_weight_init", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_atomic_seen"], ), weight_loader=weight_loaders.NoOpWeightLoader(), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_target_atomic_seen_paligemma_init", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_atomic_seen"], ), weight_loader=weight_loaders.PaliGemmaWeightLoader(), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_target_composite_seen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_composite_seen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_target_composite_unseen", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["target_composite_unseen"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_pretrain_human300_mg60", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["pretrain_human300_mg60"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=100000, decay_lr=2.5e-6, ), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), TrainConfig( name="pi0_robocasa_pretrain_human300", model=pi0.Pi0Config( max_token_len=96, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["pretrain_human300"], ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=100000, decay_lr=2.5e-6, ), num_train_steps=100000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), # # pi0-FAST counterpart of pi0_robocasa_pretrain_human300 (RoboCasa guideline: batch_size=64, num_train_steps=75_000). # RoboCasa single-arm: action_dim kept at 32 (pi0 default) so that state/action padding matches the # shipped (32,) norm stats; RobocasaOutputs trims back to the real 12-dim action at inference. # action_horizon=10, max_token_len=256 (smoke10 run showed prompts 180-210 tokens; 256 gives headroom). # `assets.asset_id` points at the precomputed mixture norm_stats (mean/std/q01/q99) generated # by `scripts/compute_norm_stats_robocasa.py`; pi0-FAST also uses quantile normalization. # TrainConfig( name="pi0_fast_robocasa_pretrain_human300", model=pi0_fast.Pi0FASTConfig( action_horizon=10, max_token_len=256, ), data=LeRobotRobocasaDataConfig( data_dirs=DATASET_SOUP_REGISTRY["pretrain_human300"], assets=AssetsConfig(asset_id="robocasa365_human300"), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_fast_base/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=75_000, decay_lr=2.5e-6, ), num_train_steps=75_000, save_interval=5000, keep_period=10000, batch_size=64, num_workers=4, ), # # Single-task fine-tune: PickPlaceCounterToCabinet, target split (500 human demos). # Starts from the pi0-FAST human300 checkpoint and reuses its quantile norm stats # (pi0-FAST needs q01/q99; the single-dataset fallback only yields mean/std). # TrainConfig( name="pi0_fast_robocasa_target_PickPlaceCounterToCabinet", model=pi0_fast.Pi0FASTConfig( action_horizon=10, max_token_len=256, ), data=LeRobotRobocasaDataConfig( data_dirs=[get_ds_meta("PickPlaceCounterToCabinet", "target", "human")], assets=AssetsConfig( assets_dir="./assets/pi0_fast_robocasa_pretrain_human300", asset_id="robocasa365_human300", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("./checkpoints_v2_transfer/74999/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=500, peak_lr=1e-5, decay_steps=20_000, decay_lr=1e-6, ), num_train_steps=20_000, save_interval=2500, keep_period=5000, batch_size=64, num_workers=4, ), # # Low-memory LoRA variant of the above (fits a single 80GB GPU): LoRA on the LLM, frozen base, no EMA. # TrainConfig( name="pi0_fast_robocasa_target_PickPlaceCounterToCabinet_lora", model=pi0_fast.Pi0FASTConfig( action_horizon=10, max_token_len=256, paligemma_variant="gemma_2b_lora", ), data=LeRobotRobocasaDataConfig( data_dirs=[get_ds_meta("PickPlaceCounterToCabinet", "target", "human")], assets=AssetsConfig( assets_dir="./assets/pi0_fast_robocasa_pretrain_human300", asset_id="robocasa365_human300", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("./checkpoints_v2_transfer/74999/params"), freeze_filter=pi0_fast.Pi0FASTConfig( action_horizon=10, max_token_len=256, paligemma_variant="gemma_2b_lora", ).get_freeze_filter(), ema_decay=None, lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=500, peak_lr=5e-5, decay_steps=20_000, decay_lr=5e-6, ), num_train_steps=20_000, save_interval=2500, keep_period=5000, batch_size=32, num_workers=4, ), # # pi0.5 single-task fine-tune on RoboCasa365 PickPlaceCounterToCabinet (target split, 500 human demos). # pi0.5: discretized state goes into the language prompt, flow timestep via adaRMSNorm; quantile norm # (needs q01/q99, so we reuse the human300 mixture stats). Starts from the released pi05_base weights. # TrainConfig( name="pi05_robocasa_target_PickPlaceCounterToCabinet", model=pi0.Pi0Config(pi05=True, action_horizon=10, max_token_len=200), data=LeRobotRobocasaDataConfig( data_dirs=[get_ds_meta("PickPlaceCounterToCabinet", "target", "human")], assets=AssetsConfig( assets_dir="./assets/pi0_fast_robocasa_pretrain_human300", asset_id="robocasa365_human300", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"), lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=20_000, decay_lr=2.5e-6, ), num_train_steps=20_000, save_interval=2500, keep_period=5000, batch_size=64, num_workers=4, ), # # EVAL-ONLY config for the ctc502 60k BASE checkpoint (pi05_ctc502_60k / ctc502_59999_rawparams). # # Added 2026-09-22 after checking what that checkpoint was ACTUALLY trained with, rather # than trusting EVAL_SEEDS.md. Evidence (upstream repo Ronaldo-GOAT/pi05-neg-mesh, file # training/openpi/src/openpi/training/config.py, entry # "pi05_robocasa_pickplace_counter_to_cabinet_502_60k"): # * action_horizon=50, action_dim=32 -- NOT the 10 that # pi05_robocasa_target_PickPlaceCounterToCabinet declares. Since the client sizes its # flow-matching noise from the server's advertised action_horizon, serving at 10 both # changes the noise tensor and runs the action expert at a sequence length the model # never saw. # * no AssetsConfig override and repo_id=None -> asset_id=None -> the run fell through to # norm stats computed from the 502-demo dataset itself, and Orbax therefore wrote them # FLAT to /assets/norm_stats.json. asset_id=None here reproduces that: # create_trained_policy() loads /assets/norm_stats.json directly. # * upstream never assigns use_quantile_norm -> it stayed False -> z-score. Hence # force_zscore_norm=True. (That is also why the checkpoint's own norm_stats.json has # q01/q99 = null: nothing needed quantiles.) # Verified numerically: the checkpoint's flat norm_stats.json and this repo's # assets/robocasa_ctc502/ctc502_qnorm/norm_stats.json agree on every REAL dimension # (state 0-15, actions 0-11) to 4.4e-4 / 1.1e-7 -- they are the same ctc502 statistics. # They differ only on the PADDING dims (state 16-31, actions 12-31), where the flat file # uses std=1.0 (a no-op) and ctc502_qnorm uses std=0.0. By contrast robocasa365_human300 is # a different distribution entirely (state.mean differs by 0.86), so EVAL_SEEDS.md's # instruction to serve this checkpoint under robocasa365_human300 is wrong. # # NOT for training -- there is no weight_loader and no data here on purpose. TrainConfig( name="pi05_robocasa_ctc502_60k_eval", model=pi0.Pi0Config(pi05=True, action_dim=32, action_horizon=50, max_token_len=200), data=LeRobotRobocasaDataConfig( data_dirs=None, # eval-only: no dataset on the eval nodes, and the # norm-stat fallback must NOT fire (see force_zscore_norm) force_zscore_norm=True, # z-score, as trained upstream ), ema_decay=None, # the ctc502 lineage is raw non-EMA params num_train_steps=1, batch_size=1, num_workers=0, ), # VACE object-swap augmentation: 8 neg-mesh hard objects swapped into PnPCounterToCabinet # (vace-only, 256 eps = 8 objects x 32). Full fine-tune on the swap dataset for 30k steps. # # 2026-09-21: RETARGETED onto the ctc502 lineage, and now the SECOND ARM of a two-arm # experiment -- identical in every respect to the mimicgen_aug256 entry below except the # dataset, so the two are directly comparable. See that entry for why each of # action_horizon=50 / ema_decay=None / the ctc502_qnorm assets / the ctc502 60k # weight_loader is required; the same reasoning applies verbatim here. # The earlier note that "the robocasa365_human300 norm stats still apply" is no longer # true of this entry: the ctc502 weights were trained under ctc502_qnorm. TrainConfig( name="pi05_robocasa_target_PickPlaceCounterToCabinet_vace_negmesh", model=pi0.Pi0Config(pi05=True, action_horizon=50, max_token_len=200), data=LeRobotRobocasaDataConfig( data_dirs=[ { "path": "/scratch/jonghoon/datasets/lerobot_swap_negmesh256_allintra_drop13", "horizon": 500, "filter_key": "500_demos", "task": "PickPlaceCounterToCabinet", "split": "target", "source": "human", } ], assets=AssetsConfig( assets_dir="./assets/robocasa_ctc502", asset_id="ctc502_qnorm", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_ctc502_60k/params"), ema_decay=None, lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=30_000, decay_lr=2.5e-6, ), num_train_steps=30_001, save_steps=(1_000, 1_500, 3_000, 5_000, 10_000, 15_000, 20_000, 25_000, 30_000), save_interval=2500, # ignored while save_steps is set keep_period=500, batch_size=64, fsdp_devices=1, num_workers=4, ), # MimicGen augmentation on the same 8 neg-mesh hard objects (mlnha/mimicgen-pi05-aug256, # 256 eps, natural distribution -- AluminumFoil006 yielded 0 of 640 attempts so 7 objects # are represented). Unlike the VACE set above these are NEW rollouts, so the published # release ships only rendered video + raw MimicGen HDF5; the LeRobot parquet is rebuilt by # jobs/mimicgen_build_lerobot.py, which drives robocasa's own reorder_hdf5_action / # reorder_hdf5_state so the simulator's arm-first layout is mapped onto this modality.json # rather than copied through (copying through is what scored 0/160 in the sibling GR00T run). # State/action/modality end up byte-compatible with the negmesh set. # # 2026-09-21: RETARGETED onto the ctc502 lineage. Four deliberate departures from the # negmesh config above -- all four are required together, none is cosmetic: # action_horizon=50 openpi's stock Pi0Config default (models/pi0_config.py), and what # the ctc502 lineage and the documented eval protocol both use. # action_dim stays at its 32 default. # ema_decay=None the ctc502 lineage disables EMA deliberately -- the EMA copy was # found broken for this task family, and the eval protocol reads raw # non-EMA params. (Without this it would inherit 0.99.) # assets ctc502_qnorm the ctc502 weights were trained under ctc502_qnorm normalisation, # whose quantiles differ materially from human300 (state q01[0] # +0.0296 vs -0.1537). Wrong stats here corrupt the warm start. # NB: the norm_stats.json shipped INSIDE pi05_ctc502_60k_rawparams/ # has q01/q99 = null and is unusable for pi0.5 quantile norm; the # file installed under ./assets/robocasa_ctc502/ctc502_qnorm comes # from Ronaldo-GOAT/pi05-neg-mesh and has all four fields, 32-D. # weight_loader warm start from the ctc502 60k export instead of pi05_base. It is # params-only (no train_state), so this is warm-start-only: never # --resume from it. jobs/ctc502_prep.sbatch stages it into the # openpi cache so nothing is fetched from GCS at train time. TrainConfig( name="pi05_robocasa_target_PickPlaceCounterToCabinet_mimicgen_aug256", model=pi0.Pi0Config(pi05=True, action_horizon=50, max_token_len=200), data=LeRobotRobocasaDataConfig( data_dirs=[ { "path": "/scratch/jonghoon/datasets/lerobot_mimicgen_aug256_allintra", "horizon": 500, "filter_key": "500_demos", "task": "PickPlaceCounterToCabinet", "split": "target", "source": "human", } ], assets=AssetsConfig( assets_dir="./assets/robocasa_ctc502", asset_id="ctc502_qnorm", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_ctc502_60k/params"), ema_decay=None, lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=30_000, decay_lr=2.5e-6, ), num_train_steps=30_001, save_steps=(1_000, 1_500, 3_000, 5_000, 10_000, 15_000, 20_000, 25_000, 30_000), save_interval=2500, # ignored while save_steps is set keep_period=500, batch_size=64, fsdp_devices=1, num_workers=4, ), # Pose6DAug action-augmentation on the SAME 8 neg-mesh hard objects # (Ronaldo-GOAT/transfer :: dataset/lerobot_actaug256_neg_pi05, 256 eps = 8 x 32). # THIRD ARM of the comparison. The grasps here were re-earned by the pi0.5 ctc502 60k # policy -- the very checkpoint this config warm-starts from -- so the arm differs from # its two siblings ONLY in augmentation method: # vace_negmesh VACE video object-swap # mimicgen_aug256 MimicGen trajectory generation # pose6daug_neg256 Pose6DAug simulator action-augmentation <- this entry # Same 8 objects, same 256-episode budget, same task, same recipe, same warm start, # and the same 160-episode eval bank covers all three. # # Every field below is inherited verbatim from the two sibling arms -- see the # mimicgen entry above for why action_horizon=50 / ema_decay=None / ctc502_qnorm / # the ctc502 60k weight_loader are each required. THREE things differ, all deliberate: # # num_train_steps=5_001 The user's current instruction is to stop at 5k, NOT the # 30k the repo README suggests. The +1 is load-bearing: the # loop is `range(0, num_train_steps)`, so 5_000 has to be # inside it for a genuine 5000/ checkpoint to exist rather # than a 4999/ one. # save_steps every 500 Ten checkpoints, to study EARLY learning densely. Paired # with keep_period=500: checkpoints.py hardcodes # max_to_keep=1, so keep_period is the ONLY thing that stops # each checkpoint being deleted the moment the next lands. # Every listed step is divisible by 500, so none is pruned. # decay_steps=30_000 DELIBERATELY UNCHANGED, and this is the subtle one. The # cosine still decays over 30k even though the run stops at # 5k, so these 5k steps are bit-identical to the first 5k the # other two arms saw -- which is the entire point of a # controlled third arm. The cost is that the run ends about a # third of the way down the cosine at LR ~1.9e-5 rather than # at the 2.5e-6 floor. That is intended. Do NOT "fix" it to # 5_000; ours_config_assert.py asserts it stays 30_000. TrainConfig( name="pi05_robocasa_target_PickPlaceCounterToCabinet_pose6daug_neg256", model=pi0.Pi0Config(pi05=True, action_horizon=50, max_token_len=200), data=LeRobotRobocasaDataConfig( data_dirs=[ { "path": "/scratch/jonghoon/datasets/lerobot_actaug256_neg_pi05", "horizon": 500, "filter_key": "500_demos", "task": "PickPlaceCounterToCabinet", "split": "target", "source": "human", } ], assets=AssetsConfig( assets_dir="./assets/robocasa_ctc502", asset_id="ctc502_qnorm", ), ), weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_ctc502_60k/params"), ema_decay=None, lr_schedule=_optimizer.CosineDecaySchedule( warmup_steps=1_000, peak_lr=2.5e-5, decay_steps=30_000, decay_lr=2.5e-6, ), num_train_steps=5_001, save_steps=(500, 1_000, 1_500, 2_000, 2_500, 3_000, 3_500, 4_000, 4_500, 5_000), save_interval=2500, # ignored while save_steps is set keep_period=500, batch_size=64, fsdp_devices=1, num_workers=4, ), # --- DISABLED 2026-09-21 : LoRA variant, never used. See DISABLED_CONFIGS.md --- # # Low-memory LoRA variant (single 80GB GPU): LoRA on both PaliGemma and the action expert, no EMA. # TrainConfig( # name="pi05_robocasa_target_PickPlaceCounterToCabinet_lora", # model=pi0.Pi0Config( # pi05=True, # action_horizon=10, # max_token_len=200, # paligemma_variant="gemma_2b_lora", # action_expert_variant="gemma_300m_lora", # ), # data=LeRobotRobocasaDataConfig( # data_dirs=[get_ds_meta("PickPlaceCounterToCabinet", "target", "human")], # assets=AssetsConfig( # assets_dir="./assets/pi0_fast_robocasa_pretrain_human300", # asset_id="robocasa365_human300", # ), # ), # weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"), # freeze_filter=pi0.Pi0Config( # pi05=True, # action_horizon=10, # max_token_len=200, # paligemma_variant="gemma_2b_lora", # action_expert_variant="gemma_300m_lora", # ).get_freeze_filter(), # ema_decay=None, # lr_schedule=_optimizer.CosineDecaySchedule( # warmup_steps=500, # peak_lr=5e-5, # decay_steps=20_000, # decay_lr=5e-6, # ), # num_train_steps=20_000, # save_interval=2500, # keep_period=5000, # batch_size=32, # num_workers=4, # ), ] if len({config.name for config in _CONFIGS}) != len(_CONFIGS): raise ValueError("Config names must be unique.") _CONFIGS_DICT = {config.name: config for config in _CONFIGS} def cli() -> TrainConfig: return tyro.extras.overridable_config_cli({k: (k, v) for k, v in _CONFIGS_DICT.items()}) def get_config(config_name: str) -> TrainConfig: """Get a config by name.""" if config_name not in _CONFIGS_DICT: closest = difflib.get_close_matches(config_name, _CONFIGS_DICT.keys(), n=1, cutoff=0.0) closest_str = f" Did you mean '{closest[0]}'? " if closest else "" raise ValueError(f"Config '{config_name}' not found.{closest_str}") return _CONFIGS_DICT[config_name]