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action
list
timestamp
float32
0
4.47
frame_index
int64
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134
episode_index
int64
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99
index
int64
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13.5k
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1.6
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1.9
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2
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2.1
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2.2
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2.3
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2.4
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2.5
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2.533333
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2.566667
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77
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2.6
78
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78
0
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2.633333
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79
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2.666667
80
0
80
0
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2.7
81
0
81
0
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2.733333
82
0
82
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2.766667
83
0
83
0
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2.8
84
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84
0
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2.833333
85
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85
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2.866667
86
0
86
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2.9
87
0
87
0
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2.933333
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88
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2.966667
89
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3
90
0
90
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3.033333
91
0
91
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3.066667
92
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92
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3.1
93
0
93
0
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3.133333
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94
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3.166667
95
0
95
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3.2
96
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96
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3.233333
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97
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3.266667
98
0
98
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3.3
99
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99
0
End of preview. Expand in Data Studio

Franka Pick-Cube — Flat Scene (100 episodes)

Synthetic pick-and-place dataset generated with the Genesis simulator on AMD RDNA4 (Radeon AI PRO R9700). A scripted Franka Panda arm picks up a cube from randomized table positions, recorded as two-camera video observations in LeRobot v3.0 format.

Part of the Robot Synthetic Data Generation Workshop.

Kitchen scene variant: lidavidsh/franka-pick-kitchen-100ep-genesis — same task with a realistic rustic kitchen GLB environment (~209 MB).

Dataset Overview

Item Value
Robot Franka Panda 7-DOF
Task Pick up a cube from a random position on the table
Scene Flat ground plane
Episodes 100
Frames per episode 135
Total frames 13,500
FPS 30
Cameras 2 (up + side), 640×480, AV1 video
Format LeRobot v3.0
Size ~80 MB

Scene Comparison

Flat Scene (this dataset) Kitchen Scene
Dataset this dataset franka-pick-kitchen-100ep-genesis
Environment Plain ground plane Realistic rustic kitchen (GLB mesh)
Visual complexity Low (solid colors) High (textured surfaces, furniture)
Size ~80 MB ~209 MB
Script 01_gen_data.py 02_gen_data_custom_scene.py

Generation Details

Item Value
Simulator Genesis 0.4.5
GPU AMD Radeon AI PRO R9700 (RDNA4, gfx1201)
ROCm 7.2.0
Rendering EGL + Mesa radeonsi (hardware GPU rasterization)
Random seed 42
Cube X range [0.4, 0.7] m
Cube Y range [-0.2, 0.2] m
Success rate 100/100 = 100%
Generation time 629s (~6.3s/ep)

The dataset was generated using:

python scripts/01_gen_data.py \
  --n-episodes 100 \
  --repo-id local/rdna4-video-100ep \
  --fps 30 \
  --no-bbox-detection

Features

Feature Shape Type
observation.state (9,) float32 — 7 joint pos + 2 gripper pos
action (9,) float32 — 7 joint pos + 2 gripper pos
observation.images.up (3, 480, 640) video (AV1)
observation.images.side (3, 480, 640) video (AV1)

Usage

Load with LeRobot

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("lidavidsh/franka-pick-100ep-genesis")
print(f"Episodes: {dataset.meta.total_episodes}, Frames: {len(dataset)}")

sample = dataset[0]
print(sample["observation.state"].shape)   # torch.Size([9])
print(sample["observation.images.up"].shape)  # torch.Size([3, 480, 640])

Train SmolVLA

python scripts/02_train_vla.py \
  --dataset-id lidavidsh/franka-pick-100ep-genesis \
  --n-steps 2000 \
  --batch-size 4 \
  --num-workers 4 \
  --save-dir outputs/smolvla_genesis

Requirements

  • lerobot==0.4.4
  • torchcodec>=0.2.1 (for video decoding; on ROCm, build from source to avoid CUDA dependency)
  • PyTorch 2.x

Benchmark Results (RDNA4)

Config Training Time Per-step GPU Utilization
Video nw=4 24.5 min 0.73 s/step 96%

License

Apache 2.0

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