Robot piperx_bimanual — 14-D joint state/action (per arm: 6 joints + gripper × 2)
3 RGB cameras: cam_front, cam_left_wrist, cam_right_wrist (resized to 224×224)
Data: 60 demos / 23,087 frames @ 30 Hz, LeRobot v2.1

Training

Base: Physical Intelligence pi05_base · full fine-tune (no LoRA) · 3.35B params
10,000 steps · batch 32 · action_horizon 50 · AdamW · cosine LR (warmup 600 → peak 5e-5 → 5e-6) · EMA 0.99
State input: discrete tokens · normalization: quantile (q01–q99)
Final flow-matching train loss: 0.0012
Hardware: 1 × NVIDIA RTX PRO 6000 Blackwell (96 GB)

params/ — EMA inference weights (orbax)
assets/piperx_bimanual/norm_stats.json — normalization stats (baked from this dataset)
_CHECKPOINT_METADATA
The 29 GB optimizer train_state/ is intentionally excluded (not needed for inference).

Load & run (openpi)

Requires the piperx-openpi fork (provides piperx_policy + the pi05_piper_stacking config).

python

import pathlib
from huggingface_hub import snapshot_download
from openpi.training import config as _config
from openpi.policies import policy_config

ckpt = pathlib.Path(snapshot_download("axiboai/pi05_piper_stacking"))
cfg = _config.get_config("pi05_piper_stacking")
policy = policy_config.create_trained_policy(cfg, ckpt)

# observation = {"observation.images.cam_front": img, ...cam_left_wrist, ...cam_right_wrist,
#                "observation.state": state_14d, "prompt": "stack red cube on blue cube"}
action_chunk = policy.infer(observation)["actions"]   # (50, 14)

If pi05_piper_stacking is not yet in your src/openpi/training/config.py, add:

python

    TrainConfig(
        name="pi05_piper_stacking",
        model=pi0_config.Pi0Config(pi05=True),
        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi05_base/params"),
        data=LeRobotPiperXBimanualDataConfig(
            repo_id="axiboai/piper_stacking",
            base_config=DataConfig(prompt_from_task=True),
            assets=AssetsConfig(asset_id="piperx_bimanual"),
            default_prompt="stack red cube on blue cube",
        ),
        lr_schedule=_optimizer.CosineDecaySchedule(warmup_steps=600, peak_lr=5e-5, decay_steps=9400, decay_lr=5e-6),
        num_train_steps=10000, batch_size=32, save_interval=2500, keep_period=2500,
    ),

Provenance / license

Fine-tuned from Physical Intelligence's pi05_base checkpoint via openpi; the weights inherit the upstream openpi / base-model license. Dataset: axiboai/piper_stacking.

Checkpoints in this repo

Checkpoints from the same run (for overfitting comparison):

Path	Step	Train loss
`/` (root)	10,000 (final)	0.0012
`step_7500/`	7,500	0.0021
`step_5000/`	5,000	0.0031
`step_2500/`	2,500	0.0048

Load any with create_trained_policy(cfg, ckpt_dir / "step_XXXX") (root = final). Training loss fell monotonically (2,500 → final); compare real-rollout success to tell whether the longer run overfit the 60 demos.

axiboai/pi05_piper_stacking

π₀.₅ · piper_stacking (bimanual cube stacking)

Task & embodiment

Training

Contents

Load & run (openpi)

Provenance / license

Checkpoints in this repo

axiboai/
pi05_piper_stacking