yuantianyuan01/FastWAM

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1362 stars · 173 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch codebase for Fast-WAM, a World Action Model for robot manipulation that skips test-time future video imagination and generates actions directly from latent world representations. It includes training and evaluation code for LIBERO and RoboTwin benchmarks with LeRobot 2.1/3.0 dataset support.

Use cases

• train a world action model for robot manipulation
• evaluate VLA-style policies on LIBERO benchmarks
• run fast real-time robot policy inference without future video generation
• train on LeRobot 3.0 robot datasets
• reproduce Fast-WAM paper results on RoboTwin
• compare imagine-then-execute vs direct action generation WAMs

When to choose

• you need a fast real-time world action model for embodied control
• you want to train or evaluate policies on LIBERO or RoboTwin
• you need LeRobot dataset support for robot learning experiments

When to avoid

• you need a general-purpose video generation model
• you need a production robot control stack rather than research code
• you work outside GPU-equipped Linux environments

Facets

library · maturity active

machine-learning deep-learning llm-training simulation robotics machine-learning artificial-intelligence autonomous-vehicles python world-action-model vla robot-learning video-modeling libero robotwin lerobot research-code gpu linux

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