CLAW: Learning continuous latent action world models from action-free video via adversarial regularization

CLAW is a fully end-to-end self-supervised framework that jointly learns a world model and continuous latent action representations directly from videos without action annotations. It uses adversarial latent regularization to prevent the latent variables from encoding future observations, so that they capture action structure, and a diffusion model models environment dynamics as a distribution over future observations rather than a single deterministic rollout. The authors say this latent action world model supports imitation learning from observation and goal-directed planning, and outperforms existing methods in multi-task, multi-embodiment experiments.