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name: motus description: "Use Motus, a unified latent-action world model, for robot-video/action data preparation, CUDA inference, RoboTwin evaluation, and distributed training configuration." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Motus repository skill

Motus combines a WAN video generator, action expert, and frozen Qwen VLM in a trimodal mixture-of-tokens model for robot video, actions, and latent-action pretraining. Use this skill for operational work around the public Motus repository; it does not replace the external RoboTwin simulator or supply model checkpoints and datasets.

Route by task

• Data layouts, episode validation, camera views, normalization, conversion, or LeRobot/T5 caches: read data-preparation.
• Real-world image inference, T5 conditioning, checkpoints, or RoboTwin policy evaluation: read model-inference.
• YAML configuration, fine-tune/resume semantics, torchrun, DeepSpeed, SLURM, or checkpoint export: read training.

Read troubleshooting for cross-cutting installation, assets, optional backends, and path failures. Read provenance before deciding whether this skill matches a changed Motus checkout.

Installation and prerequisites

The source documents Python 3.10, CUDA 12.8, torch 2.7.1/torchvision 0.22.1, flash-attn, and the pinned runtime requirements. Install a compatible CUDA wheel first, then the requirements needed by the selected route. LeRobot adds its own optional package and requirements. Use an isolated environment; do not install all optional assets merely to inspect the routing skill.

Minimal import smoke:

python -c "import torch; print(torch.__version__, torch.cuda.is_available())"
python -c "from data.dataset import create_dataset, collate_fn; from models.action_expert import ActionExpertConfig; print(ActionExpertConfig())"

Actual model construction, checkpoint loading, T5 encoding, training, and RoboTwin execution require CUDA, large external assets, and substantial VRAM. The public guide estimates over 24 GB for pre-encoded-T5 inference, about 41 GB for on-the-fly T5, and over 80 GB for training. Treat CPU imports and --help checks as parser/API evidence only.

Shared operating rules

1. Replace all example paths with paths valid on the target machine. Keep WAN, VLM, VAE, checkpoint, dataset, and output roots distinct and readable.
2. Derive action_chunk_size from the common frame count and video/action frequency ratio; keep action dimensions, state dimensions, embodiment config, and normalization statistics aligned.
3. Prefer pre-encoded language embeddings when memory is constrained. Keep the selected text instruction aligned with its embedding variant.
4. Run safe validation before any network download, cache rewrite, simulator launch, or distributed job. Launches are explicit, potentially expensive operations and are never inferred from an import success.