---
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.