Atomictan/grab_tube_merged_v3

This dataset was created using LeRobot.

Dataset Description

118 teleoperated demonstrations of an SO-101 arm picking up a small tube from a mat and dropping it into a bin, recorded with two cameras (wrist and side) at 30 fps.

An ACT policy trained on this data reaches 96.7% success (29/30) on real hardware.

That number is the point of this dataset. Plenty of demonstration sets exist; few state what success rate the data actually produces, under what protocol, or how the data was assembled to get there.

Measured result

Policy	Training episodes	Success rate	Protocol
v2	50	40% (8/20, reproduced twice)	20 trials, 40 s
v3	75	60% (24/40 pooled)	2x20 trials, 40 s
v4	107	82.5% (33/40 pooled)	2x20 trials, 40 s
v5 (this dataset)	118	96.7% (29/30)	30 trials, 50 s

Trained with ACT for 57k steps. Success criterion: tube securely grasped and lifted, with tube positions deliberately varied across the workspace.

How it was built

Not one bulk recording session. The 118 episodes are 50 base demonstrations plus five rounds of targeted corrections, each round recorded after evaluating the current policy and identifying its specific failure mode. Architecture, hyperparameters and hardware were unchanged throughout -- only the data changed.

The composition is the interesting part: episodes were added to cover workspace regions and grasp situations where the policy was measurably failing, rather than to increase the count.

Usage

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("Atomictan/grab_tube_merged_v3")

Train an ACT policy on it:

lerobot-train \
  --dataset.repo_id=Atomictan/grab_tube_merged_v3 \
  --policy.type=act \
  --output_dir=outputs/train/grab_tube \
  --job_name=grab_tube \
  --policy.device=cuda

It also works as an offline buffer for RL, though note it carries no reward column -- ReplayBuffer.from_lerobot_dataset requires next.reward, so you would need to label it first (real demonstrations have no ground-truth success signal).

Known limitations

• Single task, single environment. One tube, one mat, one lighting condition. Expect a policy trained on this to be sensitive to background changes; ACT fine-tunes its ResNet18 backbone on relatively few episodes and picks up incidental scene cues.
• Tube positions are concentrated where the correction cycles focused. Coverage is good across the reachable workspace but not uniform.
• No reward labels. Success/failure is not annotated per episode.
• robot_type is recorded as so_follower, from a local fork's naming, rather than the more standard so101_follower. The data itself is stock SO-101.

Provenance and method

Recorded July 8-22, 2026 on a single hobbyist build, as part of learning robot ML end-to-end.

The method worth copying is the measurement, not the data. Casual testing of the first policy suggested roughly 70% success; a fixed protocol -- 20 trials, deliberately varied tube positions, a strict success criterion -- measured 40%. Informal testing samples the easy positions the demonstrations already covered.

From there each cycle was: evaluate under the fixed protocol, tag every failure with a category and workspace zone, form a hypothesis about the dominant failure mode, and record corrections targeting only that. One hypothesis (background/scene drift between recording and evaluation) was tested by restoring the original scene and re-running the protocol; it moved success 35% -> 40%, within noise, and was rejected before any training time was spent on it. The position-coverage hypothesis survived, and drove the remaining cycles.

Full writeup, including the failure taxonomy and what each correction round targeted: GRASP_IMPROVEMENT_SUMMARY.md. The wider project log -- imitation learning, simulation, and reinforcement learning on the same arm -- is in PROJECT_LOG.md.

• Homepage: https://github.com/atomictan/lerobot-so101
• Paper: [More Information Needed]
• License: apache-2.0

Dataset Structure

meta/info.json:

{
    "codebase_version": "v3.0",
    "fps": 30,
    "features": {
        "action": {
            "dtype": "float32",
            "names": [
                "shoulder_pan.pos",
                "shoulder_lift.pos",
                "elbow_flex.pos",
                "wrist_flex.pos",
                "wrist_roll.pos",
                "gripper.pos"
            ],
            "shape": [
                6
            ]
        },
        "observation.state": {
            "dtype": "float32",
            "names": [
                "shoulder_pan.pos",
                "shoulder_lift.pos",
                "elbow_flex.pos",
                "wrist_flex.pos",
                "wrist_roll.pos",
                "gripper.pos"
            ],
            "shape": [
                6
            ]
        },
        "observation.images.wrist": {
            "dtype": "video",
            "shape": [
                640,
                480,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "is_depth_map": false,
                "video.height": 640,
                "video.width": 480,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {}
            }
        },
        "observation.images.side": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "is_depth_map": false,
                "video.height": 480,
                "video.width": 640,
                "video.codec": "av1",
                "video.pix_fmt": "yuv420p",
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false,
                "video.g": 2,
                "video.crf": 30,
                "video.preset": 12,
                "video.fast_decode": 0,
                "video.video_backend": "pyav",
                "video.extra_options": {}
            }
        },
        "timestamp": {
            "dtype": "float32",
            "shape": [
                1
            ],
            "names": null
        },
        "frame_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "episode_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        },
        "task_index": {
            "dtype": "int64",
            "shape": [
                1
            ],
            "names": null
        }
    },
    "total_episodes": 118,
    "total_frames": 95305,
    "total_tasks": 1,
    "chunks_size": 1000,
    "data_files_size_in_mb": 100,
    "video_files_size_in_mb": 200,
    "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
    "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
    "robot_type": "so_follower",
    "splits": {
        "train": "0:118"
    }
}

Citation

BibTeX:

@misc{grab_tube_so101_2026,
  title  = {Grab the Tube: 118 SO-101 demonstrations reaching 96.7% with ACT},
  author = {Atomictan},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Atomictan/grab_tube_merged_v3}
}