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
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:
+---------------------------------+
| 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.