SmolWAM: エッジデバイス向けロボット操作のための潜在ワールドアクションモデル
SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices
大規模モデルの事前学習済み部分を再利用し軽量化した潜在ワールドアクションモデルで、エッジデバイス上でも高速にロボット操作を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Timi Owolabi, Hikmah Olawore, Frances Adelakun, Ayotomiwa Oyewumi
分類: cs.RO, cs.LG
原文アブストラクト
World-action models (WAMs) predict a robot's actions together with the observations those actions will produce. However, the strongest models rely on video diffusion transformers that are far too computationally expensive for resource-constrained edge devices. We present SmolWAM, a latent WAM that we derive from the 2B-parameter Cosmos Policy by keeping its pretrained trunk and rebuilding its inference interface: single-frame visual latents, cached text features that preserve the trunk's attention sinks, 12 of 28 transformer blocks, and 1,008 tokens from which actions and future visual latents are read out in one rectified-flow step. Trained in 3.6 GPU-hours, SmolWAM succeeds in 95.5% of 400 LIBERO episodes at 57 ms and 2.3 TFLOP per 16-action chunk with 2.4 GiB of GPU memory, 11x faster than its teacher with 18.5x fewer FLOPs and 3.2x less memory, and its world head still forecasts scene motion.