issacdataengine/policy/openpi-InternData-A1/docs/norm_stats.md

Normalization Statistics

Here we provide instructions for computing normalization statistics for both real-world, simulation (InternData-A1) and sim2real tasks. The computed statistics are saved in JSON format and are intended to be reused during training and evaluation in the OpenPI pipeline.

Normalization is computed over:

• state
• actions

and follows the exact data preprocessing and repacking logic used during training.

---

1. Simulation Tasks (InternData-A1)

This script scripts/compute_norm_stats_sim.py computes normalization statistics for simulation tasks in the InternData-A1 benchmark.

Supported Robots

• split_aloha
• lift2
• genie1
• franka

Dataset Structure

Download the InternData-A1 datasets from here. The structure of the dataset is as follows:

InternData-A1/sim/
└── <task_category>/
    └── <robot_name>/
        └── <task_name>/               # no subtask
            ├── data/
            ├── meta/
            └── videos/

Some tasks may have subtasks / collections:

InternData-A1/sim/
└── <task_category>/
    └── <robot_name>/
        └── <task_name>/
            └── <collect_name>/
                ├── data/
                ├── meta/
                └── videos/

Usage

python scripts/compute_norm_stats_sim.py \
  --root_data_dir InternData-A1/sim \
  --task_category pick_and_place_tasks \
  --save_path stats/sim \
  --start_ratio 0.0 \
  --end_ratio 1.0

Arguments

• root_data_dir: Root directory of simulation datasets.
• task_category: Task category to process (e.g. pick_and_place_tasks).
• save_path: Root directory where normalization statistics will be saved.
• start_ratio, end_ratio: Fraction of tasks to process (useful for sharding large datasets).

Output Structure

<save_path>/
└── <task_category>/
    └── <robot_name>/
        └── <task_name>/
            └── <collect_name>/   # empty if no subtask
                └── norm_stats.json

During pretraining, set the stats_dir argument in DataConfig to the save_path here.

2. Real-World Tasks

This script scripts/compute_norm_stats_real.py computes normalization statistics for real-world tasks.

Supported Robots

• lift2
• split_aloha
• acone
• genie1

Dataset Structure

Real-world datasets are expected to follow the LeRobot repository structure:

InternData-A1/real/
    └── <robot_name>/
        └── <task_name>/
            └── <collect_name>/   # empty if no subtask
                ├── data/
                ├── meta/
                └── videos/

Example task path:

InternData-A1/real/genie1/
└── Pick_a_bag_of_bread_with_the_left_arm__then_handover/set_0

Usage

python scripts/compute_norm_stats_real.py \
  --task_path InternData-A1/real/genie1/Pick_a_bag_of_bread_with_the_left_arm__then_handover/* \
  --robot_name genie1 \
  --save_path stats/real

Arguments

• task_path: Path (or glob pattern) to a real-world task dataset(e.g. InternData-A1/real/genie1/Pick_a_bag_of_bread_with_the_left_arm__then_handover/*)
• robot_name: Robot platform name (must be supported).
• save_path: Root directory where normalization statistics will be saved.

Output Structure

<save_path>/
└── <robot_name>/
    └── <task_name>/
        └── norm_stats.json

During finetuning, set the fixed_stats_dir argument in DataConfig to <save_path>/<robot_name>/<task_name> here.

3. Sim2Real Experiments

This script scripts/compute_norm_stats_sim2real.py computes normalization statistics for sim2real experiments.

Supported Robots

• lift2

Dataset Structure

Dataset from InternData-A1 are expected to follow the LeRobot repository structure:

InternData-A1/sim/
    └── <task_category>/
        └── <robot_name>/
            └── <task_name>/
                └── <collect_name>/
                    ├── data/
                    ├── meta/
                    └── videos/

Example task path:

InternData-A1/sim/long_horizon_tasks/lift2/
└── sort_the_rubbish
    └── Sort_rubbish_1l2r
    └── Sort_rubbish_2l1r
    └── Sort_rubbish_2l2r

Usage

python scripts/compute_norm_stats_sim2real.py \
  --task_path InternData-A1/sim/long_horizon_tasks/lift2/sort_the_rubbish/* \
  --robot_name lift2 \
  --save_path stats/sim2real

Arguments

• task_path: Path (or glob pattern) to a task dataset(e.g. InternData-A1/sim/long_horizon_tasks/lift2/sort_the_rubbish/* means training on all the collections in the task)
• robot_name: Robot platform name (we only support lift2 for now, but you can try other robots).
• save_path: Root directory where normalization statistics will be saved.

Output Structure

<save_path>/
└── <robot_name>/
    └── <task_name>/
        └── norm_stats.json

During finetuning, set the fixed_stats_dir argument in DataConfig to <save_path>/<robot_name>/<task_name> here.

Implementation Notes

For simulation tasks and sim2real experiments, computation may stop early (e.g. after 10k steps) to limit runtime.

For sim2real transfer, we set the gripper dimension in the state vector to zero because the state of the gripper in the real world during inference is not aligned with the state in the simulation. See src/openpi/policies/sim2real_split_aloha_policy.py for more details.