issacdataengine/docs_crawled/policy_training.md

Source: https://internrobotics.github.io/InternDataEngine-Docs/policy/training.html

Training ​

This guide covers data format conversion and policy training for validating generated simulation data.

Part 1: LMDB to LeRobot Data Conversion ​

The simulation data generated by InternDataEngine is stored in LMDB format. To use this data for policy training, you need to convert it to LeRobot format.

Step 1: Install LeRobot v2.1 ​

We use LeRobot v2.1 format for data storage. Install the LeRobot 2.1 repo.

Step 2: Convert LMDB to LeRobot v2.1 ​

Use the conversion scripts in ``policy/lmdb2lerobotv21directory.

We provide conversion scripts for different robot platforms:

• **lmdb2lerobot_lift2_a1.py **( script ): Lift2 (ARX).
• **lmdb2lerobot_split_aloha_a1.py **( script ): Split Aloha.
• **lmdb2lerobot_genie1_a1.py **( script ): Genie1.
• **lmdb2lerobot_franka_a1.py **( script ): Franka FR3.
• **lmdb2lerobot_frankarobotiq_a1.py **( script ): Franka with Robotiq gripper.

Example usage: bash

python lmdb2lerobot_lift2_a1.py \
    --src_path ${src_path} \
    --save_path ${save_path} \
    --repo_id ${repo_id} \
    --num-threads ${num_threads} \
    --num_demos ${num_demos}

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**Parameters: **

• **--src_path **( str ): Path to the source LMDB data directory.
• **--save_path **( str ): Path to save the converted LeRobot dataset.
• **--repo_id **( str ): Dataset repository identifier.
• **--num-threads **( int ): Number of threads for parallel processing.
• **--num_demos **( int ): Number of demonstrations to convert (optional).

Step 3: Convert to LeRobot v3.0 (Optional) ​

If you need LeRobot v3.0 format for training, please install LeRobot 3.0. Then use the conversion script: bash

python convertv21_to_v30.py --input_path ${v21_path} --output_path ${v30_path}

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The conversion code is available at ``policy/lmdb2lerobotv21/convertv21_to_v30.py.

Part 2: Policy Training with π 0 ​

As described in the InternData-A1 paper, we used multi-machine, multi-GPU JAX-based π 0 for data validation.

We have implemented a JAX-based, multi-nodes, multi-GPU training pipeline that supports multi-dataset mixed training for π 0 .

Features ​

• **Multi-machine, multi-GPU training **: Scale training across multiple nodes
• **Multi-dataset mixed training **: Train on multiple datasets simultaneously
• **JAX-based implementation **: High-performance training with JAX/Flax

Installation, Training, and Deployment ​

For detailed instructions on installation, training, and deployment, please refer to the openpi-InternData-A1 README.

References ​

• LeRobot- HuggingFace LeRobot
• InternData-A1 Paper- InternData-A1: A High-Fidelity Synthetic Data Generator for Robotic Manipulation
• openpi-InternData-A1- JAX-based π 0 training code