HSC-VLA：分层场景清除用于密集杂乱环境中的鲁棒双臂操作
HSC-VLA: Hierarchical Scene-Clearing for Robust Bimanual Manipulation in Dense Clutter
AI总结：
HSC-VLA 通过分层场景清除方法提升密集杂乱环境中的双臂操作鲁棒性，实现 86.7% 的高成功率。
摘要：
现代视觉-语言-动作模型在高密度操作环境中常常出现关键的指令遵循失败，因为任务无关的视觉杂乱稀释了注意力，破坏了语义关联，并显著降低了复杂长时间 horizon 场景中的性能。为克服单一大端到端架构的表示瓶颈，我们提出了 HSC-VLA，一个分层框架，通过显式的场景清除抽象将高层视觉-语义推理与低层高频的传感器运动执行解耦。HSC-VLA 使用一个高层大脑来分解长时间 horizon 任务，并生成任务特定的场景掩码，以保留任务相关的几何结构并抑制干扰项。过滤后的观察随后传递给低层小脑，一个基于扩散的策略，仅使用掩码过滤的视觉和本体感觉进行双臂操作。在密集杂乱的超市货架上的大量实验表明，HSC-VLA 在高密度杂乱环境中实现了 86.7% 的总体成功率，超过了最佳的单一大端到端基线（π₀-Full FT 为 34.3%）的 52.4%。HSC-VLA 还表现出强大的长时间 horizon 性能，在杂乱排序任务中达到 72%，在补货任务中达到 66%，展示了在复杂杂乱操作中的强大鲁棒性和有效的故障恢复能力。
英文摘要：
Modern Vision--Language--Action models often suffer from critical instruction-following failures in high-density manipulation environments, where task-irrelevant visual clutter dilutes attention, corrupts grounding, and substantially degrades performance in complex long-horizon scenarios. To overcome the representation bottleneck of monolithic end-to-end architectures, we propose HSC-VLA, a hierarchical framework that decouples high-level visual-semantic reasoning from low-level, high-frequency sensorimotor execution through an explicit scene-clearing abstraction. HSC-VLA employs a high-level Brain to decompose long-horizon tasks and to generate task-specific scene masks that preserve task-relevant geometry while suppressing distractors. The filtered observations are then passed to a low-level Cerebellum, a diffusion-based policy that performs bimanual manipulation using only mask-filtered vision and proprioception. Extensive experiments in densely cluttered supermarket shelves demonstrate that HSC-VLA achieves 86.7\% aggregate success under high-density clutter, surpassing the best monolithic baseline ($π_0$-Full FT at 34.3\%) by 52.4\%. HSC-VLA also exhibits strong long-horizon performance, reaching 72\% on clutter sorting and 66\% on restocking, demonstrating strong robustness and effective failure recovery in complex cluttered manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
