UniLAP

A Unified Latent Action Policy for Cross-Embodiment Manipulation

UniLAP predicts robot action chunks from visual observations, language instructions, and robot state. This repository provides robot-specific checkpoints containing the latent action policy and action decoder, together with the configuration and normalization statistics needed for inference.

Project and paper information · Companion code

Checkpoints

Directory	Robot	Benchmark	Tasks
bridge/	WidowX	SimplerEnv	Cube stacking, carrot placement, spoon placement, eggplant placement
fractal/	Google Robot	SimplerEnv	Move near, pick up a coke can, open and close drawers
gr1/	GR1	RoboCasa	Pick-and-place tasks with a bottle, can, cup, milk, potato, and wine

The Google Robot checkpoint is shared by the Visual Matching (VM) and Variant Aggregation (VA) evaluations.

Files

UniLAP/
├── README.md
├── bridge/
│   ├── config.yaml
│   └── model.ckpt
├── fractal/
│   ├── config.yaml
│   └── model.ckpt
├── gr1/
│   ├── config.yaml
│   └── model.ckpt
└── metadata/
    ├── bridge_stats.json
    ├── fractal_stats.json
    └── gr1_stats.json

• model.ckpt: latent policy and action decoder weights.
• config.yaml: model dimensions, pretrained encoder identifiers, action representation, and normalization settings used by the inference loader.
• metadata/*_stats.json: action and state normalization statistics.

Keep each checkpoint beside its config.yaml and preserve the directory layout.

Download

Use the Hugging Face CLI. Run these commands from the companion code directory.

Download all checkpoints:

hf download RayYoh/UniLAP --local-dir checkpoints/UniLAP

Or download one robot and its supporting files:

# WidowX
hf download RayYoh/UniLAP \
  bridge/model.ckpt bridge/config.yaml metadata/bridge_stats.json README.md \
  --local-dir checkpoints/UniLAP

# Google Robot
hf download RayYoh/UniLAP \
  fractal/model.ckpt fractal/config.yaml metadata/fractal_stats.json README.md \
  --local-dir checkpoints/UniLAP

# GR1
hf download RayYoh/UniLAP \
  gr1/model.ckpt gr1/config.yaml metadata/gr1_stats.json README.md \
  --local-dir checkpoints/UniLAP

Inference and evaluation

The companion code release is being prepared at RayYoh/UniLAP. The commands below require a code checkout containing eval/run_lap_server.py and scripts/eval_paper.sh. Follow its installation instructions for the unilap policy environment and eval/envs/README.md for the simpler and gr1 simulator environments.

Run from the code checkout root:

conda activate unilap
CHECKPOINT_ROOT="$(pwd)/checkpoints/UniLAP"

To start a policy server independently, select bridge, fractal, or gr1:

ROBOT=bridge
python eval/run_lap_server.py \
  --ckpt_path "$CHECKPOINT_ROOT/$ROBOT/model.ckpt" \
  --stat_path "$CHECKPOINT_ROOT/metadata/${ROBOT}_stats.json" \
  --robot "$ROBOT" \
  --num_inference_steps 4 \
  --device cuda \
  --port 5555

The server loads DINOv2 ViT-B/14, SigLIP ViT-B/16 (vit_base_patch16_siglip_224.v2_webli), and Flan-T5-large through the companion code. Their pretrained files must be available locally or downloadable on first use.

For closed-loop evaluation, use the following entrypoints. They start the policy server and simulator client automatically. For SimplerEnv, set SIMPLER_PATH to your checkout containing the RGB overlay assets.

export SIMPLER_PATH=/path/to/SimplerEnv

# WidowX
STAT_PATH="$CHECKPOINT_ROOT/metadata/bridge_stats.json" \
  bash scripts/eval_paper.sh bridge "$CHECKPOINT_ROOT/bridge/model.ckpt"

# Google Robot: Visual Matching
STAT_PATH="$CHECKPOINT_ROOT/metadata/fractal_stats.json" \
  bash scripts/eval_paper.sh fractal_vm "$CHECKPOINT_ROOT/fractal/model.ckpt"

# Google Robot: Variant Aggregation
STAT_PATH="$CHECKPOINT_ROOT/metadata/fractal_stats.json" \
  bash scripts/eval_paper.sh fractal_va "$CHECKPOINT_ROOT/fractal/model.ckpt"

# GR1
STAT_PATH="$CHECKPOINT_ROOT/metadata/gr1_stats.json" \
  bash scripts/eval_paper.sh gr1 "$CHECKPOINT_ROOT/gr1/model.ckpt"

Citation

Please cite UniLAP when using these checkpoints in your research. See the project page for publication updates.

@misc{unilap2026,
  title = {UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation},
  author = {Lei Yao and Qiao Feng and Mutian Tong and Qihao Yun and Lap-Pui Chau and Lingjie Liu},
  year = {2026},
  url = {https://rayyoh.github.io/UniLAP/}
}

License

The UniLAP-authored weights, configurations, and documentation in this repository are licensed under Creative Commons Attribution-NonCommercial 4.0 International. This permits noncommercial use, sharing, and adaptation with attribution under the license terms.

The companion code uses the MIT License. Referenced pretrained models, datasets, and simulators remain subject to their respective original licenses and terms.