| --- |
| license: other |
| license_name: slim-license |
| license_link: https://github.com/kzz1031/SLIM/blob/main/LICENSE |
| library_name: slim-policy |
| tags: |
| - robotics |
| - robot-manipulation |
| - flow-matching |
| - self-supervised-learning |
| - libero |
| --- |
| |
| # SLIM for LIBERO |
|
|
| This repository contains the released SLIM Stage 2 policy checkpoint for |
| LIBERO and LIBERO-Plus. SLIM is a compact latent interaction policy for robot |
| manipulation. |
|
|
| ## Checkpoint |
|
|
| - Stage 1: action-grounded masked trajectory prediction on LIBERO all+90 |
| - Stage 1 objective: IDM:FDM = 0.125:1 for 3 epochs |
| - Stage 2: flow-matching policy training on LIBERO all for 40 epochs |
| - Stage 1 and Stage 2 video backend: `torchvision_av` |
| - Stage 1 EMA: enabled, momentum 0.999 |
| - Stage 2 EMA: disabled |
| - Action horizon and execution chunk: 8 |
| - Image size: 224 x 224, agent and wrist views |
| - State/action dimensions: 7/7 |
|
|
| The checkpoint is a plain PyTorch `state_dict` and loads directly with |
| [SLIM](https://github.com/kzz1031/SLIM). |
|
|
| ## Results |
|
|
| | Benchmark | Coverage | Score | |
| | --- | ---: | ---: | |
| | LIBERO | 2,000 / 2,000 | 97.50% | |
| | LIBERO-Plus | 10,030 / 10,030 | 77.45% | |
|
|
| LIBERO suite scores are 94.40% (LIBERO-10), 99.40% (Spatial), 99.40% |
| (Object), and 96.80% (Goal). The complete LIBERO-Plus suite/category reports |
| are included under `evaluation/`. |
|
|
| ## Usage |
|
|
| Install SLIM and configure the DINOv2 and T5 paths as described in the SLIM |
| README. Then run a policy server from the SLIM repository root: |
|
|
| ```bash |
| python -m slim.serving.server \ |
| --checkpoint /path/to/SLIM-LIBERO/checkpoints/epoch_40_pytorch_model.pt \ |
| --port 10093 \ |
| --bf16 |
| ``` |
|
|
| The checkpoint requires the included `config.yaml` and `action_stats.json` to |
| remain in the repository root. See `checkpoint_manifest.json` for hashes and |
| the exact release revision. |
|
|
| ## Limitations |
|
|
| This checkpoint is intended for research evaluation in LIBERO-compatible |
| simulation environments. It should not be deployed on physical robots without |
| task-specific safety validation and action-bound checks. |
|
|
