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91965fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # PI0.5 few-shot checkpoint: `global_step_8000`
This directory contains the RLinf-native PI0.5 checkpoint saved at training
step **8,000** from the `Pi05_few_shot_libero` few-shot run. It is an
independent, inference-ready checkpoint and includes its own normalization
statistics and consolidated LIBERO evaluation results.
## Model description
- Base policy: PI0.5 / OpenPI, config name `pi05_libero`
- Training data: 10 selected episodes per task, sampling seed `42`
- Suites represented in the run: LIBERO-Spatial, LIBERO-Object, LIBERO-Goal,
and LIBERO-10
- Action dimension: `7` (six robot motion dimensions plus one gripper
dimension)
- Gripper convention: normalized value **`0 = close`, `1 = open`**
- Inference: 10 denoising steps, noise level `0.5`, five action chunks per
inference
The gripper convention is part of the model/evaluation contract. Keep the
seventh action value in `[0, 1]` and do not reverse its meaning in a custom
adapter.
## Evaluation result
The checkpoint was evaluated on all four suites with 50 episodes per task,
500 trajectories per suite, fixed ordered reset states, and evaluation seed
`195`. The aggregate success-once rate is **0.670**
(1340/2000).
| Suite | Trajectories | Success-once | Success-at-end |
|---|---:|---:|---:|
| LIBERO-Spatial | 500 | 0.782 | 0.170 |
| LIBERO-Object | 500 | 0.848 | 0.158 |
| LIBERO-Goal | 500 | 0.658 | 0.114 |
| LIBERO-10 | 500 | 0.392 | 0.054 |
The complete reusable parameters are in [`eval/summary.json`](./eval/summary.json),
and per-task results are in the suite subdirectories under `eval/`.
## Files and loading
- `actor/model_state_dict/full_weights.pt`: complete model state dict
- `physical-intelligence/libero/norm_stats.json`: required normalization
statistics for this checkpoint
- `eval/summary.json`: evaluation parameters and aggregate metrics
- `rlinf_model_config.yaml`: portable RLinf model fragment
Set the checkpoint directory before evaluation:
```bash
export PI05_FEWSHOT_CHECKPOINT_DIR=/absolute/path/to/Pi05_few_shot_libero/global_step_8000
```
Then use the included [`rlinf_model_config.yaml`](./rlinf_model_config.yaml) and
keep `openpi_data.norm_stats_path` pointed at the normalization file in this
directory.
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