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pi05-openarm-rh56f1-teleopv4-baseline-20k
RyanL22/pi05-openarm-rh56f1-teleopv4-baseline-20k
pi05-openarm-rh56f1-teleopv4-baseline-20k is a robotics model from RyanL22. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for lerobot. The card lists the license as apache-2.0.
LeRobot-native pi05 (v0.6.1) fine-tuned only on real teleoperation data: OpenArm teleop v4, the 261 episodes recorded at 20 Hz (ball 70 / bottle 48 / box 88 / doll 55, 87,905 frames). No synthetic / human-video data.…
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Model card
From the Hugging Face model README
pi0.5 baseline — OpenArm + RH56F1, real teleop only (teleop v4, 20 Hz), step 20k
LeRobot-native pi05 (v0.6.1) fine-tuned only on real teleoperation data: OpenArm teleop v4,
the 261 episodes recorded at 20 Hz (ball 70 / bottle 48 / box 88 / doll 55, 87,905 frames).
No synthetic / human-video data. This is the teleop-only baseline for the anyh2r experiments.
Checkpoint: step 20,000 (final) of a 20,000-step run (lab-gpu26, 2x H100, batch 32 x 2 = 64).
Training settings
| value | |
|---|---|
| vision encoder (SigLIP, 412.4M) | fine-tuned (not frozen) |
| image augmentation | photometric + affine, one draw replayed across the stereo pair |
| cell shares | ∝ sqrt(frames), 4 cells |
| mirror augmentation | off |
| batch | 32 per GPU x 2 GPUs = 64 (same global batch as the 16 x 4 runs) |
| optimizer | AdamW, peak lr 2.5e-5, cosine decay to 2.5e-6, warmup 1000 |
| precision | bfloat16, gradient checkpointing |
| chunk | 50 actions @ 20 fps (2.5 s), n_obs_steps=1 |
| normalization stats | constant dims (head pitch/yaw) widened to mean ± 0.1 rad |
Unlike the ...-0916-* checkpoints, the neck quantiles are widened here, so a sub-degree encoder
offset on the (fixed) head no longer normalizes to a huge value. Overwriting the state neck with
0.889 / 0.001 is still harmless.
Inputs
observation.images.base_0_rgb<- left ZED view, 288x512observation.images.left_wrist_0_rgb<- right ZED view, 288x512observation.state— 28 dims:neck(2) | left_arm(7) | right_arm(7) | left_hand(6) | right_hand(6)action— same 28-dim layout, absolute joint targets
Tasks: pick up the {ball | bottle | small box | doll} from table and put it in the white box.
Load
from lerobot.policies.pi05.modeling_pi05 import PI05Policy
policy = PI05Policy.from_pretrained("RyanL22/pi05-openarm-rh56f1-teleopv4-baseline-20k")
- Task
- Robotics
- Library
- lerobot
- License
- apache-2.0
- Created
- Sep 19, 2026
- Updated
- Sep 19, 2026