File size: 1,652 Bytes
0b5a17c 01bd936 0b5a17c | 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 | ---
library_name: pytorch
tags:
- robotics
- imitation-learning
- diffusion-policy
- libero
---
# StreamingWAM LIBERO checkpoint
This repository contains the released StreamingWAM checkpoint trained on the
four standard LIBERO suites and evaluated on LIBERO-Plus. The checkpoint is
intended for use with the accompanying
[StreamingWAM code](https://github.com/renshaojie233/StreamingWAM).
## File
| File | Size | SHA-256 |
| --- | ---: | --- |
| `streamingwam_libero.pt` | 12,042,077,420 bytes | `7b321c0c85c6167fda5eb21f7e1fe6abe745f9b77be20eef0144b97ae2d6aa45` |
The PyTorch checkpoint contains the model weights (`mot` and
`proprio_encoder`) and minimal loading metadata (`step` and `torch_dtype`). It
does not contain optimizer state, scheduler state, random-number-generator
state, training logs, or experiment-tracking data.
## Usage
Download `streamingwam_libero.pt`, install the StreamingWAM codebase, and pass
the local checkpoint path to the evaluation command:
```bash
python experiments/libero/eval_libero_task_list_multi_k.py \
task=streamingwam_libero_plus \
ckpt=/path/to/streamingwam_libero.pt \
EVALUATION.dataset_stats_path=assets/libero_dataset_stats.json \
+EVALUATION.task_list_file=assets/libero_plus_full_10030.txt \
+EVALUATION.sdp_k_values='[4]'
```
See the code repository for the complete environment, data layout, and
evaluation options.
## Scope and terms
This checkpoint is released for research on robot learning and policy
evaluation. The source code is MIT-licensed. External datasets, pretrained
components, benchmark assets, and model weights retain their respective
licenses and terms.
|