DreamMachines/actuator_unboxing_1h_HQ_dagger_intv

Actuator unboxing, one high-quality hour plus intervention data

actuator_unboxing_1h_HQ plus the human corrections from 240 rollouts of the policy trained on that hour: 1.7 h in total, the post's cheapest large gain.

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 2.9.

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

• The 198 episodes of actuator_unboxing_1h_HQ, unchanged.
• 240 rollouts of the 1h-from-scratch policy, clearing the top tray six times, with a human intervening whenever the policy was about to make a mistake. The first tray at the evaluation position, the rest with varied box positions and orientations.
• Of those, 212 rollouts had interventions and contribute only their intervention segments; the 28 that succeeded without one contribute their full trajectory.
• 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.
• Source: the high-quality hour plus human-gated interventions during 240 rollouts of the policy trained on it, the corrections given through the VR teleop interface.
• Scenes: the lightbox; the first of the six trays at the evaluation position, the other five with varied box positions and orientations.
• Processing: corrected rollouts contribute only their intervention segments, uncorrected successes their full trajectory.

Contents

Episodes	512
Frames	304,002 (1.7 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 set: 88% (35/40) at the standard evaluation position, up from 28% for the policy trained on the hour alone, and 48% (19/40) at varied box positions, up from 11%.
• This 1.7 h model beat the model trained on 21 h by 12 points at the standard position (all 1.7 h were collected in the evaluation scene).

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: Actuator unboxing, one high-quality hour

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}
}