---
license: other
library_name: pytorch
pipeline_tag: robotics
datasets: [XDOF/ABC-130k]
tags: [long-wam, yam, abc-130k, robotics]
---

# Long-WAM YAM

A YAM bimanual robot pretraining model trained on **ABC 130K**, with a
LongLive 2.0 Robot-S video backbone. It predicts actions after imagining future
video latents.

## Download

```bash
hf download Efficient-Large-Model/Long-WAM-YAM --local-dir ./weights/Long-WAM-YAM
```

## Inference

Load `model.pt` with the matching Long-WAM runtime, `config.yaml` and
`dataset_stats.json`. Use 4 video denoising steps to sigma 0.9, followed by
10 action denoising steps.

The three RGB views use a fixed **384 × 320** layout:

```text
+---------------------------------+
|       Top view: 256 × 320        |
+----------------+----------------+
| Left wrist     | Right wrist    |
| 128 × 160      | 128 × 160      |
+----------------+----------------+
```

P48 covers **1.6 seconds at 30 Hz**. Sample history every four control steps:
13 images become four clean latent frames, followed by two imagined latents.
Predict a 32-step action chunk, approximately 1.07 seconds.

State and action are 14-D: left arm's six joints and gripper, then right arm's
six joints and gripper. Use the supplied z-score statistics to normalize state
and denormalize predicted actions; RGB inputs use `[-1, 1]`.

The matching runtime and base-model VAE/text assets are required separately.
This is a pretraining checkpoint: task-specific adaptation and controlled
closed-loop validation are required before real-robot use.