---
license: apache-2.0
tags:
  - robotics
  - pi0.5
  - openpi
  - lora
  - RA-BC
---

# pi05-bread-r1-top200 — RA-BC policy for bread-to-toaster (bimanual UMI)

pi0.5 base + LoRA finetune (10k steps, killed at loss plateau), trained with
**RA-BC** (reward-model-weighted BC, SARM2 delta-progress weights, clipped at 0)
on **200 curated bimanual UMI episodes** (top-200 of 256 by reward-model
mean-dP + dP-variance).

Training code: https://github.com/BrianZhengJourney/openpi/tree/bread-r1
(`pi05_bread_r1_lora` config; `BreadInputs`/`BreadOutputs` in
`src/openpi/policies/bread_policy.py`). Data pipeline + decision docs:
https://github.com/BrianZhengJourney/bread-toaster.

## Contents

- `10000/params` — orbax params (servable with openpi `create_trained_policy`)
- `10000/assets` — **norm stats (quantile), computed on the 200 training episodes.
  The policy is meaningless without them; loaded automatically from the ckpt dir.**

## Inference contract (must match EXACTLY at deployment)

| Item | Value |
|---|---|
| Obs images | `left_wrist_image`, `right_wrist_image` — uint8 HWC, resize-with-pad → 224×224 (done inside the transform stack). **No base/top camera: the base_0_rgb slot is zeroed + masked** (training top view was a human egocam; do not feed the RealSense) |
| Obs state | 20D `[Lxyz, Lrot6d, Lgrip, Rxyz, Rrot6d, Rgrip]`, **relative_to_first**: per arm `T_rel = inv(T_arm,start) @ T_arm,t` (record each arm's grasp_site pose at rollout start, re-express every subsequent pose). Frame = URDF `grasp_site` (yam_linear_4310 canonical TCP). Gripper: 0=closed, 1=open |
| Prompt | `take the bread out of the bowl and put the bread into the toaster` (exact string) |
| Action | `(40, 14)` @30 Hz: per arm `[Δxyz(3, TCP frame), axis-angle Δrot(3), ABSOLUTE gripper target(1)]`, **stepwise-chained**: `delta_k = inv(T_{t+k-1}) @ T_{t+k}`; executor integrates `T_cmd,k = T_cmd,k-1 @ delta_k` starting from `T_current`. SO(3) composition only; denormalize exactly once |
| Latency | UMI-style: feed the proprio sampled at image exposure time; start executing the chunk at step `ceil(latency × 30 Hz)` |

## Minimal loading example (openpi, branch `bread-r1`)

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

policy = policy_config.create_trained_policy(
    _config.get_config("pi05_bread_r1_lora"), "<this-repo-local-path>/10000")
out = policy.infer({
    "left_wrist_image": left_uint8_hwc, "right_wrist_image": right_uint8_hwc,
    "state": rel_state_20d,
    "prompt": "take the bread out of the bowl and put the bread into the toaster",
})
actions = out["actions"][:, :14]   # (40, 14)
```

## Provenance

- Base: `gs://openpi-assets/checkpoints/pi05_base` · LoRA gemma_2b_lora + gemma_300m_lora · bs 32 · lr 2.5e-5 cosine · stopped at 10k/20k (plateau; lr was ~50% peak — resume from `10000` if undertrained on-robot)
- Reward model for weights: SARM2 r4_bimanual (v3 7-stage labels, human-verified boundaries)
- wandb: project `openpi`, run `r1_top200` (8d6mivlw)
- Offline P0 check results: see the repo issue/notes accompanying this upload