STORM：基于槽的任务感知面向对象的机器人操作表示
STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation
AI总结：
提出STORM模块，通过多阶段训练策略将冻结的视觉基础模型与语义感知槽结合，生成面向对象的任务感知表示，提升机器人操作在视觉干扰下的泛化性和控制性能。
摘要：
视觉基础模型为机器人提供了强大的感知特征，但其密集表示缺乏显式的对象级结构，限制了操作任务的鲁棒性和可收缩性。我们提出STORM（基于槽的任务感知面向对象的机器人操作表示），一个轻量级的面向对象适应模块，通过一组语义感知槽增强冻结的视觉基础模型，用于机器人操作。STORM不重新训练大型骨干网络，而是采用多阶段训练策略：首先通过使用语言嵌入的视觉-语义预训练稳定面向对象的槽，然后与下游操作策略联合适应。这种分阶段学习防止了退化槽的形成，并在保持语义一致性的同时将感知与任务目标对齐。在对象发现基准和模拟操作任务上的实验表明，与直接使用冻结的基础模型特征或端到端训练面向对象的表示相比，STORM改善了对视觉干扰物的泛化能力和控制性能。我们的结果强调了多阶段适应作为将通用基础模型特征转化为用于机器人控制的任务感知面向对象表示的有效机制。
英文摘要：
Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limiting robustness and controllability in manipulation tasks. We propose STORM (Slot-based Task-aware Object-centric Representation for robotic Manipulation), a lightweight object-centric adaptation module that augments frozen visual foundation models with a small set of task-aware slots for robotic manipulation. Rather than fully tuning large backbones on the task, STORM employs an efficient two-stage training strategy: few layers of object-centric representation are first trained on top of the frozen backbone through visual--semantic pretraining using language embeddings, then jointly adapted with a downstream manipulation policy for task alignement. This staged learning prevents degenerate slot formation and preserves semantic consistency while aligning perception with task objectives. Experiments on object discovery benchmarks and robotic manipulation tasks show that STORM improves control performance and generalization to visual shifts (distractors, textures, lighting) compared to directly using frozen or fine-tuned foundation model features, or existing object-centric representations. STORM serves not only as an efficient mechanism for refining generic foundation model features, but also as a novel way of injecting beneficial structural and semantic bias into policy learning.
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