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上級arXiv

RM-WAM: Reliable Memory-Augmented World Action Models for Long-Horizon Robot Manipulation

Cui Miao, Junhao Yu, Ping Liu, Ning Yang, Xuhua Chen, He Zheng, Xiaodong Wang, Tao Zhang
日本語版は未提供のため、英語原文で表示しています。
要旨より

World Action Models (WAMs) improve robotic manipulation by jointly predicting future visual states and actions, yet most existing methods make decisions primarily from the current observation. This limits their reliability in long-horizon tasks, where visually similar states may correspond to different interaction histories and task stages.

要旨より。当社による要約は作成中です。