# Inference Setup for new_v3 Foundation + Edited Checkpoints

This repo's foundation ckpt (`mixed_new_knife_v3/best_val/600/`) and all `edits/*` ckpts
share the **same** model arch and **same** norm_stats (`real_pass_knife_new_mixed_train`).
They are **not** compatible with the original `pi05_real_pk_mixed_v2` config's norm_stats
(differences up to ~28 % on q99 — predictions will be visibly off if you use the wrong
norm stats).

## Setup (one-time, on the inference machine)

### 1. Add the TrainConfig entry to openpi

Open `openpi/src/openpi/training/config.py` and paste the contents of `inference_config_snippet.py`
(in this folder) into the `_CONFIGS` list. The entry registers `pi05_real_pk_mixed_new_v3`
which points the data loader at the new norm_stats source.

### 2. Place norm_stats where openpi expects them

```bash
# Pull the bundled norm_stats from the foundation ckpt (already in this repo)
mkdir -p ~/openpi/assets/pi05_real_pk_mixed_new_v3
hf download haohw/realrobot-openpi-new-knife-v3 \
    --include "mixed_new_knife_v3/best_val/600/assets/real_pass_knife_new_mixed_train/norm_stats.json" \
    --local-dir /tmp/_pull
mv /tmp/_pull/mixed_new_knife_v3/best_val/600/assets/real_pass_knife_new_mixed_train \
   ~/openpi/assets/pi05_real_pk_mixed_new_v3/
```

Verify: `~/openpi/assets/pi05_real_pk_mixed_new_v3/real_pass_knife_new_mixed_train/norm_stats.json` exists.

### 3. (Optional) Get the LeRobot eval dataset

If you want to run `scripts/eval_*` or load batches at inference, materialize the eval set:

```bash
hf download rollingoat/mutil_mode_data new_knife_lerobot_all.zip \
    --repo-type dataset --local-dir /mnt/data/new_knife_downloads
# extract + symlink as `~/.cache/huggingface/lerobot/real_pass_knife_mixed_v2_eval`
# (see the original build_pk_v2_split.py for the exact 10-ep split if needed)
```

For pure inference (no batched eval), step 3 is not required — you can feed observations
directly via openpi's policy serving API.

## Running inference

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

cfg = _config.get_config("pi05_real_pk_mixed_new_v3")
policy = policy_config.create_trained_policy(
    cfg, checkpoint_dir="/path/to/mixed_new_knife_v3/best_val/600",
)
# action = policy.infer(observation)
```

Edited ckpt (any of the `edits/*` folders):
```python
policy = policy_config.create_trained_policy(
    cfg, checkpoint_dir="/path/to/edits/action_v4_keep_left_step100",
)
```

(The same TrainConfig works for the foundation and all edits — they only differ in the
`params/` weights, not in arch or norm stats.)

## Sanity check before robot rollout

```python
import jax, numpy as np
from openpi.training import config as _config
from openpi.training import data_loader as _data_loader

cfg = _config.get_config("pi05_real_pk_mixed_new_v3")
dc = cfg.data.create(cfg.assets_dirs, cfg.model)
print("asset_id:", dc.asset_id)
# should print: real_pass_knife_new_mixed_train
print("norm_stats keys:", list(dc.norm_stats))
# should include 'state' and 'actions'
```

If `asset_id` prints `real_pass_knife_mixed_v2_train` instead of `..._new_mixed_train`,
the asset_id override didn't take — re-check the snippet was pasted with `assets=AssetsConfig(asset_id=...)`
on the **outer** RealRobotDataConfig (not inside `base_config`).