Base: gs://openpi-assets/checkpoints/pi05_base/params
Train config: pi05_so101_lora (PaliGemma 2B LoRA + Gemma 300M LoRA action expert)
Action horizon: 10 (≈0.33 s @ 30 Hz)
Batch size: 32
LR schedule: cosine, peak 5e-5, warmup 500 steps, decay over 10k
Optimizer: AdamW with grad clip 1.0

Inference inputs (per step)

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observation.state              : float32 (6,)  joint pos (5 arm + 1 gripper, 0=open/100=closed)
observation.images.camera1     : uint8        wrist camera   → maps to `left_wrist_0_rgb`
observation.images.camera2     : uint8        overhead camera → maps to `base_0_rgb`
prompt                         : "Pick up the red block and place it"

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python

from openpi.policies import policy_config
from openpi.training import config as _config

cfg = _config.get_config("pi05_so101_lora")
policy = policy_config.create_trained_policy(cfg, "step_15000")

Each step folder contains params/, assets/, and _CHECKPOINT_METADATA — the minimum required by create_trained_policy. The train_state/ optimizer state is not included.

jakegonz/pi05-so101-lora-50demos

pi05-so101-lora-50demos

Training config

Inference inputs (per step)

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jakegonz/
pi05-so101-lora-50demos