Datasets:

cmriat
/

lefold-preview

license: mit
task_categories:
  - robotics
  - reinforcement-learning
language: []
pretty_name: LeFold Preview
size_categories:
  - 10M<n<100M
tags:
  - lerobot
  - so101
  - dual-arm
  - manipulation
  - folding
  - cloth

LeFold Preview

This is a preview dataset for LeFold, a dual-arm cloth folding manipulation dataset collected with the SO-101 dual-arm robot. The dataset is built using the LeRobot v3.0 format and is fully compatible with LeRobot training pipelines.

Dataset Summary

Property	Value
Robot	SO-101 Dual Arm (`so101_dualarm`)
Total Episodes	1,200
Total Frames	738,533
Number of Tasks	4
FPS	30
Action Dim	12 (6 per arm)
State Dim	12 (6 joint positions per arm)
Arm Spacing	46 cm (center-to-center of two arm bases)

Task Categories

Task Index	Task	Episodes	Frames
0	Fold the long pants (长裤折叠)	300	162,084
1	Fold the long sleeves (长袖折叠)	300	246,170
2	Fold the short pants (短裤折叠)	300	90,854
3	Fold the short sleeves (短袖折叠)	300	239,425

Observation Space

Cameras

Camera Key	Resolution	Codec	Description
`observation.images.left_wrist`	480×640	AV1 (yuv420p)	Left wrist-mounted camera
`observation.images.right_wrist`	480×640	AV1 (yuv420p)	Right wrist-mounted camera
`observation.images.right_front`	720×1280	AV1 (yuv420p)	Right front view camera

State

Joint positions (float32 × 12):

Index	Joint Name
0	`left.shoulder_pan.pos`
1	`left.shoulder_lift.pos`
2	`left.elbow_flex.pos`
3	`left.wrist_flex.pos`
4	`left.wrist_roll.pos`
5	`left.gripper.pos`
6	`right.shoulder_pan.pos`
7	`right.shoulder_lift.pos`
8	`right.elbow_flex.pos`
9	`right.wrist_flex.pos`
10	`right.wrist_roll.pos`
11	`right.gripper.pos`

Action Space

12-dimensional continuous action (same structure as state, 6 DoF per arm).

Splits

Split	Episodes
Train	0–1200

Usage

from lerobot.datasets import LeRobotDataset

dataset = LeRobotDataset(
    repo_id="cmriat/lefold-preview",
    split="train",
)

Or download directly via huggingface-cli:

huggingface-cli download cmriat/lefold-preview --repo-type dataset

Training

First install LeRobot with training dependencies:

pip install lerobot[training]

Method 1: Training Config File (Recommended)

Create a config file train_lefold.yaml:

dataset:
  repo_id: cmriat/lefold-preview
  video_backend: pyav

policy:
  type: pi05                   # PI0.5 — flow-matching vision-language-action policy
  device: cuda
  push_to_hub: false
  pretrained_path: lerobot/pi05_base   # fine-tune from PI05 base checkpoint

  paligemma_variant: gemma_2b
  action_expert_variant: gemma_300m
  dtype: bfloat16

  chunk_size: 30                # action prediction horizon
  n_obs_steps: 1
  max_state_dim: 32
  max_action_dim: 32
  image_resolution: [224, 224]

  use_relative_actions: true    # predict delta joint positions
  relative_exclude_joints: ["gripper"]

  n_action_steps: 10            # execute first 10 predicted actions
  num_inference_steps: 10       # flow-matching inference steps

  input_features:
    observation.state:
      type: STATE
      shape: [12]
    observation.images.left_wrist:
      type: VISUAL
      shape: [3, 224, 224]
    observation.images.right_front:
      type: VISUAL
      shape: [3, 224, 224]
    observation.images.right_wrist:
      type: VISUAL
      shape: [3, 224, 224]

  output_features:
    action:
      type: ACTION
      shape: [12]

batch_size: 16
steps: 60000
save_freq: 5000
log_freq: 100
num_workers: 8

optimizer:
  type: adamw
  lr: 3e-5
  betas: [0.9, 0.95]
  eps: 1e-8
  weight_decay: 0.01
  grad_clip_norm: 1.0

scheduler:
  type: cosine_decay_with_warmup
  num_warmup_steps: 1200
  num_decay_steps: 60000
  peak_lr: 3e-5
  decay_lr: 3e-6

wandb:
  enable: true
  project: lefold-so101
  disable_artifact: true

output_dir: outputs/lefold_pi05
job_name: lefold_pi05

Then launch training:

lerobot-train --config_path train_lefold.yaml

Method 2: CLI Only

For quick experiments without a config file:

lerobot-train \
    --dataset.repo_id=cmriat/lefold-preview \
    --policy.type=pi05 \
    --policy.pretrained_path=lerobot/pi05_base \
    --policy.device=cuda \
    --policy.use_relative_actions=true \
    --batch_size=16 \
    --steps=60000 \
    --output_dir=outputs/lefold_pi05 \
    --wandb.enable=true \
    --wandb.project=lefold-so101

For other supported policy types (e.g., ACT, Diffusion, multi-task DIT, SmolVLA), replace --policy.type above or refer to the LeRobot documentation.

Note on SO-101 hardware setup:

The two arms of the SO-101 dual-arm robot are mounted with their base centers 46 cm apart. When deploying a trained policy, ensure your hardware setup matches this spacing for consistent kinematics.

The front camera is positioned between the two arms, and the camera views are aligned via RGB image matching (i.e., adjusting camera pose until the RGB frames from each camera are visually consistent with the dataset).

License

MIT