Why is extra_delta_action=True in 'pi0_libero' config?

        # 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.

        # LIBERO already represents actions as deltas, but we have some old Pi0 checkpoints that are trained with this
        # extra delta transform.
        if self.extra_delta_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)],
            )

    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_config.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,
            ),
            extra_delta_transform=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,
    ),