pi0.5 — dex-warehouse full fine-tune (20-DoF)

Full fine-tune of π₀.₅ (VLM backbone + SigLIP vision encoder + action expert, 4.14 B trainable params) from lerobot/pi05_base on Kasra99/dex-warehouse — teleoperated warehouse pick-and-place on a Dexmate Vega-1 Pro mobile manipulator.

Checkpoints are stored per step as step_XXXXXX/, each containing EMA weights (decay 0.99, openpi-style), which are the weights intended for inference.

from lerobot.policies.pi05 import PI05Policy
policy = PI05Policy.from_pretrained("Kasra99/pi05_dex_warehouse_full", subfolder="step_030000")

Action space (20-DoF)

arm_center_z, L_arm_j1..j7, R_arm_j1..j7, right_hand.{open_close,thumb_opposition}_ratio, base_{vx,vy,wz}.

The two left-hand dims present in the raw dataset were dropped: the left hand is never actuated (exactly 0.0 in 217,846 / 219,260 frames), and its q01–q99 span of ~0.01 made pi0.5's QUANTILES normalization map the rare closures to +199, injecting targets ~200x normal scale into the flow-matching loss. After removal the worst normalized action is ~10.

Cameras are named for the slots pi05_base was pretrained with: observation.images.base_0_rgb (head), .left_wrist_0_rgb, .right_wrist_0_rgb.

Training


base	`lerobot/pi05_base` (lerobot 0.6.2)
trainable	full — backbone + vision encoder + action expert
precision	fp32 master weights, bf16 autocast
batch / steps	32 / 30,000 (≈4.4 epochs)
lr	2.5e-5 peak, cosine → 2.5e-6, 1k warmup
chunk size	50 (1.67 s @ 30 fps)
EMA	0.99 constant
hardware	1x RTX PRO 6000 Blackwell, ~32 h

Limitations

This is right-arm manipulation on a mobile base, not bimanual manipulation. Measured over all 177 episodes: the left arm has 1.75x the path length of the right (81.0 vs 50.7 rad/episode) but 2.6x lower cross-episode reproducibility (R² 0.087 vs 0.226) and higher jitter, and its hand never actuates — the signature of an idle teleoperator controller rather than task work. The left-arm joints are retained in the action space but should not be expected to produce purposeful motion.

The base is the most reproducible channel (R² 0.274) and is commanded in ~34% of frames.

Two of the 22 task strings have only one episode each and were held out entirely by the 5% per-task eval split, so they have no training data.