any4lerobot/robomind2lerobot

RoboMIND to LeRobot

RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view observations, proprioceptive robot state information, and linguistic task descriptions.. (Copied from docs)

⚠️ Dirty Tasks

Task ID	Reason
3_eggplantOven	take - turn on, wrong order
3_eggplantoven_2	take - turn on, wrong order
5_eggoven_2	no instruction
10_packplate	no plate marker, no plate 2
10_packplate_2	no plate marker, no plate 2
11_brushcup	two cups
12_packcup	no cup marker
13_packbowl	no bowl marker, blue - greeen flipped
35_putcarrot	no instruction
36_putpepper	no instruction
37_putegg	no instruction
39_puttomato	no instruction
40_putavocado	no instruction
41_putplum	no instruction
42_putkiwifruite	wrong word: wifruite
43_packplate	last object should be "them", not "it"
44_putbluebowlongreenplate	last object should be "them", not "it"
45_putgreenbowlonblueplate	only one instruction, but two sub-tasks
46_putredbowlonwhiteplate	only one instruction, but two sub-tasks
48_putpotatogreenplatefromsteam_2	last action is about "left arm", not "right arm"
52_holdercup	wrong order: should be right - left - right
53_stackcup	wrong order: should be right - left - right
to be continued ...

🚀 What's New in This Script

In this dataset, we have made several key improvements:

• Preservation of RoboMIND’s Original Information 🧠: We have preserved as much of RoboMIND’s original information as possible, with field names strictly adhering to the original dataset’s naming conventions to ensure compatibility and consistency.
• State and Action as Dictionaries 🧾: The traditional one-dimensional state and action have been transformed into dictionaries, allowing for greater flexibility in designing custom states and actions, enabling modular and scalable handling.

Dataset Structure of meta/info.json:

{
  "codebase_version": "v2.1", // lastest lerobot format
  "robot_type": "franka_3rgb", // specific robot type
  "fps": 30, // control frequency
  "features": {
    "observation.images.image_key": {
        "dtype": "video",
        "shape": [
            720,
            1280,
            3
        ],
        "names": [
            "height",
            "width",
            "rgb"
        ],
        "info": {
            "video.height": 720,
            "video.width": 1280,
            "video.codec": "av1",
            "video.pix_fmt": "yuv420p",
            "video.is_depth_map": false,
            "video.fps": 30,
            "video.channels": 3,
            "has_audio": false
        }
    },
    // for more states key, see configs
    "observation.states.end_effector": {
        "dtype": "float32",
        "shape": [
            6
        ],
        "names": {
            "motors": [
                "x",
                "y",
                "z",
                "r",
                "p",
                "y"
            ]
        }
    },
    ...
    // for more actions key, see configs
    "actions.joint_position": {
        "dtype": "float32",
        "shape": [
            8
        ],
        "names": {
            "motors": [
                "joint_0",
                "joint_1",
                "joint_2",
                "joint_3",
                "joint_4",
                "joint_5",
                "joint_6",
                "gripper"
            ]
        }
    },
    ...
  }
}

Installation

1. Install LeRobot:
   Follow instructions in official repo.

2. Install others:
   We use 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 "ray[default]"
   

Get started

Important

1. If you want to save depth when converting the dataset, modify _assert_type_and_shape() function in lerobot.common.datasets.compute_stats.py.

def _assert_type_and_shape(stats_list: list[dict[str, dict]]):
    for i in range(len(stats_list)):
        for fkey in stats_list[i]:
            for k, v in stats_list[i][fkey].items():
                if not isinstance(v, np.ndarray):
                    raise ValueError(
                        f"Stats must be composed of numpy array, but key '{k}' of feature '{fkey}' is of type '{type(v)}' instead."
                    )
                if v.ndim == 0:
                    raise ValueError("Number of dimensions must be at least 1, and is 0 instead.")
                if k == "count" and v.shape != (1,):
                    raise ValueError(f"Shape of 'count' must be (1), but is {v.shape} instead.")
                # bypass depth check
                if "image" in fkey and k != "count":
                    if "depth" not in fkey and v.shape != (3, 1, 1):
                        raise ValueError(f"Shape of '{k}' must be (3,1,1), but is {v.shape} instead.")
                    if "depth" in fkey and v.shape != (1, 1, 1):
                        raise ValueError(f"Shape of '{k}' must be (1,1,1), but is {v.shape} instead.")

2. The dataset needs to be organized into the following format before running the script due to differences in storage formats across platforms:

/path/to/robomind/
├── benchmark1_0_release
│   ├── h5_agilex_3rgb
│   │   ├── 10_packplate
│   │   ├── ...
│   ├── h5_franka_1rgb
│   │   ├── bread_in_basket
│   └── ...
├── benchmark1_1_release
│   ├── h5_agilex_3rgb
│   │   ├── 20_takecorn_2
│   │   ├── ...
│   ├── h5_franka_3rgb
│   │   ├── apples_placed_on_a_ceramic_plate
│   └── ...
├── benchmark1_2_release
│   ├── h5_franka_3rgb
│   │   └── 241223_upright_cup
│   └── h5_sim_franka_3rgb
│       ├── 408-place_upright_mug_on_the_left_middle
│       ├── ...
├── language_description_annotation_json
│   ├── h5_agilex_3rgb.json
│   ├── h5_franka_1rgb.json
│   ├── h5_franka_3rgb.json
│   ├── h5_simulation_franka.json
│   ├── h5_tienkung_xsens.json
│   └── h5_ur_1rgb.json
└── RoboMIND_v1_2_instr.csv

Note

The conversion speed of this script is limited by the performance of the physical machine running it, including CPU cores and memory. We recommend using 2 CPU cores per task for optimal performance. However, each task requires approximately 10 GiB of memory. To avoid running out of memory, you may need to increase the number of CPU cores per task depending on your system’s available memory.

Download source code:

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

Modify path in convert.sh:

There are three benchmarks, each with several embodiments, including agilex_3rgb, franka_1rgb, franka_3rgb, franka_fr3_dual, tienkung_gello_1rgb, tienkung_prod1_gello_1rgb, tienkung_xsens_1rgb, ur_1rgb.

python robomind_h5.py \
    --src-path /path/to/robomind/ \
    --output-path /path/to/local \
    --benchmark benchmark1_1_release \
    --embodiments agilex_3rgb franka_1rgb \
    --cpus-per-task 2

Execute the script:

For single node

bash convert.sh

For multi nodes

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.