DreamMachines/actuator_unboxing_1h_HQ

Actuator unboxing, one high-quality hour

One additional hour collected in the lightbox under strict constraints: consistent motion throughout and insertions that clear the tray edges. Episodes are roughly twice as long as in the main collection.

One of the nine training datasets behind the Dream Machines post Empirical results fine-tuning π0.5 on a real manufacturing task (September 2026), which fine-tunes π0.5 on this task and reports what mattered: training duration, LoRA vs. full fine-tuning, data quantity, scene diversity, data quality, intervention data and serving settings. Used in Section 2.8 (both arms) and Section 2.9 (as the base policy's training set).

The task

Actuators arrive in cardboard boxes and must be transferred into a tray that fixes their position and orientation for a downstream production line. Each actuator is inserted with its bottom contacts correctly oriented, with both possible 180° rotations acceptable. Only the top layer of the source box is used: 40 actuators. The left arm picks an actuator from the box and hands it to the right arm, which inserts it into one of the tray's 135 holes, arranged in 9 rows of 15. The order is fixed: actuators leave the box right to left and bottom to top, and fill the tray left to right and bottom to top. Every episode starts and ends in a rest position.

The post describes the task, the collection and the evaluation protocol in full: Section 1.

This dataset

• Collected after the main set as a data-quality experiment: is one carefully executed hour worth more than many ordinary ones?
• Task string: "Take an actuator from the box and place it in the tray".

Collection

• Robot: bimanual TRLC-DK1 (bi_dk1_follower), recorded at 50 Hz.
• Cameras: three fisheye cameras (left wrist, right wrist, top context) recorded at 640×480 MJPG and centre-cropped to 224×224 in this export, matching π0.5's SigLIP input size.
• Teleoperation: recorded after the main collection with deliberately consistent motion and insertions that clear the tray edges (the main set's fast handovers were its most common failure mode); episodes are roughly twice as long.
• Scenes: the lightbox only; box and tray positions randomised as in the main collection.
• Processing: idle periods at the start and end are trimmed.

Contents

Episodes	198
Frames	174,308 (1.0 h at 50 Hz)
Cameras	3: left_wrist, right_wrist, top, 224×224
State / action	14-dim: six joint positions and one gripper per arm, absolute joint targets
Format	LeRobot v3.0

Results in the post

• Fine-tuning π0.5 on this hour alone, 10k steps: 28% (11/40).
• Continuing the 21 h baseline checkpoint on it for 5k steps at lr 2e-5: 90% (36/40), and 98% (39/40) with tuned real-time-chunking settings.

All evaluations: 40 rollouts per round in the lightbox, one per actuator of the top layer, end-to-end success with Wilson 95% intervals; see the evaluation protocol.

Related datasets

• DreamMachines/actuator_unboxing_20h_345_v2: Actuator unboxing, 21 h (main training set)
• DreamMachines/actuator_unboxing_20h_345: Actuator unboxing, 21 h (earlier export)
• DreamMachines/actuator_unboxing_10h_vr: Actuator unboxing, operator 1 (VR teleoperation), 10 h
• DreamMachines/actuator_unboxing_10h_leaderarms: Actuator unboxing, operator 2 (Gello leader arms), 10 h
• DreamMachines/actuator_unboxing_10h_diverse: Actuator unboxing, 10 h across operators and scenes
• DreamMachines/actuator_unboxing_4h_diverse: Actuator unboxing, 4 h across operators and scenes
• DreamMachines/actuator_unboxing_4h_lightbox: Actuator unboxing, 4 h from the lightbox only
• DreamMachines/actuator_unboxing_1h_HQ_dagger_intv: Actuator unboxing, one high-quality hour plus intervention data

Citation

@misc{dreammachines2026pi05,
  title  = {Empirical results fine-tuning π0.5 on a real manufacturing task},
  author = {Dream Machines},
  year   = {2026},
  url    = {https://dream-machines.eu/blog/pi05-fine-tuning}
}