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, 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.
Replan every 24 executed 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.
Runs of Efficient-Large-Model Long-WAM-RoboTwin2.0-IDM on huggingface.co
31
Total runs
0
24-hour runs
12
3-day runs
31
7-day runs
31
30-day runs
More Information About Long-WAM-RoboTwin2.0-IDM huggingface.co Model
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Long-WAM-RoboTwin2.0-IDM is an open source model from GitHub that offers a free installation service, and any user can find Long-WAM-RoboTwin2.0-IDM on GitHub to install. At the same time, huggingface.co provides the effect of Long-WAM-RoboTwin2.0-IDM install, users can directly use Long-WAM-RoboTwin2.0-IDM installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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