潜在アクションを意図として用いた効率的な未来想像による世界行動モデル
Latent Action as Intention Enables Efficient Future Imagination for World Action Models
ロボット制御のための世界行動モデルにおいて、テスト時の未来観測生成の遅延を削減するため、潜在アクションを意図の表現として用いるLAWAを提案。将来の観測を生成せずに効率的な未来想像を実現し、少ないデモデータや分布外シナリオでの性能を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Xiang Li, Yupeng Zheng, Songen Gu, Huailiang Ma, Feng Yu, Xian Nie, Shanshuai Yuan, Yujie Zang, Weize Li, Shuai Tian, Moyang Liu, Ya-Qin Zhang, Wenchao Ding
分類: cs.RO
原文アブストラクト
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives, especially with scarce robot demonstrations and in out-of-distribution scenarios. To bridge this gap, we introduce **LAWA**, a WAM architecture that uses compact latent actions as an operational representation of future intentions, enabling efficient test-time future imagination without generating future observations. Specifically, a discrete tokenizer enhanced by action-free pre-training produces manipulation-centric codebook targets. LAWA jointly denoises a continuous latent state anchored to these targets with executable action chunks while omitting the future-video branch at inference. On RoboCasa, LAWA achieves state-of-the-art average success rates of 65.6% and 80.8% in the few-shot and full data settings, improving over the matched Fast-WAM baseline by 9.6 and 4.5 points, respectively. It also preserves the performance level of the matched Joint-WAM variant while requiring 42.9% lower inference latency. LAWA also demonstrates competitive zero-shot robustness on LIBERO-Plus and superior performance on real-world tasks. These results show that future imagination need not be discarded: retaining it with compact latent actions yields an effective trade-off among performance, generalization, and latency. Code and models will be released.