Long-WAM: Autoregressive video pretraining for long-horizon robot decision-making
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Long-WAM proposes using autoregressive video pretraining to learn visual dynamics, then connecting video and action experts with asymmetric causal attention, enabling robots to make long-horizon decisions based on historical information. Its approach predicts future frames in latent space, generates action chunks combined with historical information, and supports streaming encoding and asynchronous execution. The above content comes from Guyuehome's analysis of Long-WAM and is a method introduction; the report does not provide validation results or performance data for this approach in actual robot tasks.
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- GuyuehomeLong-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.
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