any4lerobot/libero2lerobot/README.md

LIBERO to LeRobot

LIBERO consists of 4 task suites and 130 tasks for studying LLDM. Specifically, the tasks in 3 of the 4 task suites vary only in one type of knowledge, while the last task suite requires transfer of entangled knowledge. (Copied from docs)

🚀 What's New in This Script

In this dataset, we have made several key improvements:

• OpenVLA-based LIBERO Regeneration: Resolution enhancement, No-op action filtration, 180° RGB frame rotation, Failed trajectory filtering.
• State Data Preservation: Maintained native LIBERO state information (accessible via states.ee_state, states.joint_state and etc.).
• Robust Conversion Pipeline: Using DataTrove framework for High-speed dataset transformation and automatic failure recovery during conversion

Dataset Structure of meta/info.json:

{
  "codebase_version": "v3.0", // lastest lerobot format
  "robot_type": "franka", // specific robot type
  "fps": 20, // control frequency
  "features": {
    "observation.images.image": {
        "dtype": "video",
        "shape": [
            256,
            256,
            3
        ],
        "names": [
            "height",
            "width",
            "rgb"
        ],
        "info": {
            "video.height": 256,
            "video.width": 256,
            "video.codec": "av1",
            "video.pix_fmt": "yuv420p",
            "video.is_depth_map": false,
            "video.fps": 20,
            "video.channels": 3,
            "has_audio": false
        }
    },
    // for more states key, see configs
    "observation.state": {
        "dtype": "float32",
        "shape": [
            8
        ],
        "names": {
            "motors": [
                "x",
                "y",
                "z",
                "roll",
                "pitch",
                "yaw",
                "gripper",
                "gripper"
            ]
        }
    },
    ...
    "action": {
        "dtype": "float32",
        "shape": [
            7
        ],
        "names": {
            "motors": [
                "x",
                "y",
                "z",
                "roll",
                "pitch",
                "yaw",
                "gripper"
            ]
        }
    },
    ...
  }
}

Installation

1. Install LeRobot:
   Follow instructions in official repo.

2. Install others:
   We use datatrove[ray] for parallel conversion, significantly speeding up data processing tasks by distributing the workload across multiple cores or nodes (if any).

   pip install h5py
   pip install -U datatrove
   pip install -U "datatrove[ray]" # if you want ray features
   

Get started

Note

This script supports converting from original hdf5 to lerobot. If you want to convert from rlds to lerobot, check openx2lerobot.

Download source code:

git clone https://github.com/Tavish9/any4lerobot.git

Regenerate LIBERO Trajectory:

1. Install LIBERO dependency
2. Replace libero_90 with your target libero dataset.

python libero_utils/regenerate_libero_dataset.py \
    --resolution 256 \
    --libero_task_suite libero_90 \
    --libero_raw_data_dir /path/to/libero/datasets/libero_90 \
    --libero_target_dir /path/to/libero/datasets/libero_90_no_noops

Modify in convert.sh:

1. If you have installed datatrove[ray], we recommend using ray executor for faster conversion.
2. Increase workers and tasks-per-job if you have sufficient computing resources.
3. To merge many datasets into one, simply specify both paths like: --src-paths /path/libero_10 /path/libero_90
4. To resume from a previous conversion, provide the appropriate log directory using --resume-from-save and --resume-from-aggregate
5. If you want different image resolution, regenerate the trajectory, and change the config. (DO NOT use resize)

python libero_h5.py \
    --src-paths /path/to/libero/ \
    --output-path /path/to/local \
    --executor local \
    --tasks-per-job 3 \
    --workers 10

Execute the script:

For single node

bash convert.sh

For multi nodes (Install ray first)

Direct Access to Nodes (2 nodes in example)

On Node 1:

ray start --head --port=6379

On Node 2:

ray start --address='node_1_ip:6379'

On either Node, check the ray cluster status, and start the script

ray status
bash convert.sh

Slurm-managed System

#!/bin/bash
#SBATCH --job-name=ray-cluster
#SBATCH --ntasks=2
#SBATCH --nodes=2
#SBATCH --partition=partition

# Getting the node names
nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
nodes_array=($nodes)

head_node=${nodes_array[0]}
head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)

# if we detect a space character in the head node IP, we'll
# convert it to an ipv4 address. This step is optional.
if [[ "$head_node_ip" == *" "* ]]; then
IFS=' ' read -ra ADDR <<<"$head_node_ip"
if [[ ${#ADDR[0]} -gt 16 ]]; then
  head_node_ip=${ADDR[1]}
else
  head_node_ip=${ADDR[0]}
fi
echo "IPV6 address detected. We split the IPV4 address as $head_node_ip"
fi

port=6379
ip_head=$head_node_ip:$port
export ip_head
echo "IP Head: $ip_head"

echo "Starting HEAD at $head_node"
srun --nodes=1 --ntasks=1 -w "$head_node" \
    ray start --head \
    --node-ip-address="$head_node_ip" \
    --port=$port \
    --block &

sleep 10

# number of nodes other than the head node
worker_num=$((SLURM_JOB_NUM_NODES - 1))

for ((i = 1; i <= worker_num; i++)); do
    node_i=${nodes_array[$i]}
    echo "Starting WORKER $i at $node_i"
    srun --nodes=1 --ntasks=1 -w "$node_i" \
        ray start \
        --address "$ip_head" \
        --block &
    sleep 5
done

sleep 10

bash convert.sh

Other Community Supported Cluster Managers

See the doc for more details.