Fine-tuned
π₀.₅ on the
MolmoAct2-BimanualYAM-Dataset (3 cameras: top → left → right).
Full fine-tune (~4.1B trainable), bf16, gradient checkpointing, normalization by quantiles.
Run in two phases: 0→100k, then resumed 100k→200k.
1from lerobot.policies.pi05.modeling_pi05 import PI05Policy
2from lerobot.policies.factory import make_pre_post_processors
3
4ckpt = "helen9975/pi05-molmoact-yam"
5policy = PI05Policy.from_pretrained(ckpt)
6preprocessor, postprocessor = make_pre_post_processors(policy.config, pretrained_path=ckpt)
7
8# earlier checkpoint:
9# policy = PI05Policy.from_pretrained(ckpt, revision="step-100000")
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.