Long-WAM explained: using autoregressive video pretraining for long-horizon robot decision-making

Long-WAM learns visual dynamics through autoregressive video pretraining and uses asymmetric causal attention to connect video and action experts, enabling long-horizon decision-making based on historical information. Its approach predicts future frames in latent space, combines historical information to generate action chunks, and supports streaming encoding and asynchronous execution.