library_name: pytorch
license: other
pipeline_tag: robotics
tags:
- long-wam
- robocasa365
- idm
Long-WAM RoboCasa365
Long-WAM policy weights for RoboCasa365. IDM predicts actions after generating future-video latents.
Paper: Long-WAM: Scaling the Context of World-Action Models
Project page: nvlabs.github.io/LongLive/Long-WAM
Code: github.com/NVlabs/LongLive
Download
hf download Efficient-Large-Model/Long-WAM-RoboCasa365 --local-dir ./weights/Long-WAM-RoboCasa365
Inference
Load model.pt with the matching Long-WAM runtime, using config.yaml and
the supplied dataset_stats.json for preprocessing and normalization.
P48 context uses 48 past control steps plus the current observation: 2.4 seconds at 20 Hz, encoded into four clean latent frames.
Generate two future-video latents using 4 steps (imagination sigma 0.9), then predict a 32-step action chunk using 10 action steps. Record observations at every control step and replan after executing 32 actions. Reset history at the start of each episode.
The matching inference runtime and base-model VAE/text assets are required separately. This release contains weights and inference settings only; optimizer states and training artifacts are not included.