提出分层隐动作模型，从无动作视频中发现长期高阶技能。
Hierarchical Latent Action Model
- 用预训练低层模型提取动态模式，分层聚合为高层技能
- 在长时序上显著提升技能发现能力，优于基线模型
- 适合机器人控制与交互世界模型等需要长期规划的任务
隐动作模型（LAMs）可从无动作数据中学习，应用于机器人控制到交互式世界模型。然而，现有LAMs通常只关注短时程帧间转换，捕捉低层运动而忽视更长时间结构。实际上，无动作视频常包含时序扩展的高阶技能。本文提出HiLAM，一种分层隐动作模型，通过建模长时序信息来发现隐含技能。利用预训练的LAM作为底层提取器，将包含视频内在动态模式的隐动作序列聚合为高层隐技能。实验表明，HiLAM优于基线模型，展现出鲁棒的动态技能发现能力。
原文摘要 · Abstract (English)
Latent Action Models (LAMs) enable learning from actionless data for applications ranging from robotic control to interactive world models. However, existing LAMs typically focus on short-horizon frame transitions and capture low-level motion while overlooking longer-term temporal structure. In contrast, actionless videos often contain temporally extended and high-level skills. We present HiLAM, a hierarchical latent action model that discovers latent skills by modeling long-term temporal information. To capture these dependencies across long horizons, we utilize a pretrained LAM as a low-level extractor. This architecture aggregates latent action sequences, which contain the underlying dynamic patterns of the video, into high-level latent skills. Our experiments demonstrate that HiLAM improves over the baseline and exhibits robust dynamic skill discovery.
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