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pi0_piper_stacking

Robotics
LeRobot
openpi
vla
manipulation
bimanual
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pi0_piper_stacking / README.md
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π₀ piper_stacking BC fine-tune (10k steps, final loss 0.0064)
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metadata 
license: other
library_name: openpi
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - manipulation
  - bimanual
  - lerobot
  - openpi
datasets:
  - axiboai/piper_stacking

π₀ · piper_stacking (bimanual cube stacking)

π₀ policy fine-tuned with openpi on axiboai/piper_stacking — a bimanual PiperX robot picking up a red cube and stacking it on a blue cube.

Prompt: stack red cube on blue cube

Task & embodiment

  • 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 pi0_base · full fine-tune (no LoRA) · 3.24B params
  • 10,000 steps · batch 32 · action_horizon 50 · AdamW · cosine LR (warmup 600 → peak 5e-5 → 5e-6) · EMA 0.99
  • State input: continuous · normalization: z-score (mean/std)
  • Final flow-matching train loss: 0.0064
  • Hardware: 1 × NVIDIA RTX PRO 6000 Blackwell (96 GB)

Contents

  • 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 pi0_piper_stacking config).

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/pi0_piper_stacking"))
cfg = _config.get_config("pi0_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 pi0_piper_stacking is not yet in your src/openpi/training/config.py, add:

    TrainConfig(
        name="pi0_piper_stacking",
        model=pi0_config.Pi0Config(pi05=False),
        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_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 pi0_base checkpoint via openpi; the weights inherit the upstream openpi / base-model license. Dataset: axiboai/piper_stacking.