helen9975/pi05-molmoact-yam

π₀.₅ MolmoAct2 BimanualYAM

Fine-tuned π₀.₅ on the MolmoAct2-BimanualYAM-Dataset (3 cameras: top → left → right).

main holds the final 200,000-step checkpoint. The earlier 100k checkpoint that previously occupied main is still available at the step-100000 tag.

Checkpoint details

Setting	Value
Training step	200000 (final)
Cameras	top, left, right
Action dim	14 (absolute joints + grippers)
Chunk size	30 (@ 30 Hz)
VLM	unfrozen (freeze_vision_encoder=false, train_expert_only=false)
Base model	lerobot/pi05_base
Gripper range	[0, 1] (continuous)

Training

Full fine-tune (~4.1B trainable), bf16, gradient checkpointing, normalization by quantiles.

Hardware	16×H100 (2 nodes × 8)
Batch size	16/GPU → global 256
Schedule	peak LR 2.5e-5, 1k warmup, cosine decay to 2.5e-6 over 200k steps
Samples seen	51.2M (≈0.67 epochs)
Final train loss	≈0.028

Run in two phases: 0→100k, then resumed 100k→200k.

Available revisions

Revision	Step
main	200000
step-100000	100000

Usage (LeRobot)

from lerobot.policies.pi05.modeling_pi05 import PI05Policy
from lerobot.policies.factory import make_pre_post_processors

ckpt = "helen9975/pi05-molmoact-yam"
policy = PI05Policy.from_pretrained(ckpt)
preprocessor, postprocessor = make_pre_post_processors(policy.config, pretrained_path=ckpt)

# earlier checkpoint:
# policy = PI05Policy.from_pretrained(ckpt, revision="step-100000")

Evaluation

No evaluation has been run on this 200k checkpoint yet.

The open-loop numbers previously reported here (MSE 0.00140, MAE 0.0211) were measured on the 100k checkpoint (step-100000), on 2 held-out episodes with stride 30 and reset-each-step — a smoke test, not a benchmark.