SLIM for CALVIN
This repository contains the released SLIM Stage 2 epoch-15 policy checkpoint
for the CALVIN ABC-D benchmark. The Stage 2 run was trained for 40 epochs, and
epoch 15 was selected for the reported result. SLIM is a compact latent
interaction policy for robot manipulation.
Checkpoint
- Stage 1: action-grounded masked trajectory prediction, IDM:FDM = 0.125:1
- Stage 1 duration: 3 epochs
- Stage 2: flow-matching policy training for 40 epochs; this release uses the
checkpoint saved at epoch 15
- Action horizon and execution stride: 12
- State/action dimensions: 15/7
The checkpoint is a plain PyTorch
state_dict and loads directly with
SLIM.
Results
The paper result on 1,000 CALVIN ABC-D long-horizon sequences is an average
successful sequence length of 4.556. Success rates at sequence lengths 1
through 5 are 99.3%, 96.7%, 92.3%, 87.1%, and 80.2%.
A fresh evaluation of the released package produced an average successful
sequence length of 4.578, with success rates of 99.9%, 96.4%, 92.2%,
88.0%, and 81.3%. Machine-readable results are included under evaluation/.
Usage
Install SLIM and configure the DINOv2 and T5 paths as described in the SLIM
README. Then start a policy server from the SLIM repository root:
1python -m slim.serving.server \
2 --checkpoint /path/to/SLIM-CALVIN/checkpoints/epoch_15_pytorch_model.pt \
3 --port 10093 \
4 --bf16
Keep the included config.yaml and action_stats.json in the repository root.
See checkpoint_manifest.json for hashes and the exact SLIM revision.
Limitations
This checkpoint is intended for research evaluation in CALVIN-compatible
simulation environments. It should not be deployed on physical robots without
task-specific safety validation and action-bound checks.
