SpaceDex：在分层工作空间中的通用灵巧抓取
SpaceDex: Generalizable Dexterous Grasping in Tiered Workspaces
AI总结：
SpaceDex提出了一种分层框架，通过视觉-语言模型规划和臂手特征分离网络，提升在受限3D环境中的灵巧抓取性能，实现在100次真实测试中达到63%的成功率。
摘要：
在分层工作空间中，高自由度的灵巧手抓取仍面临挑战，由于遮挡、狭窄空间和高度依赖约束比开放桌面场景更严重。现有方法多在无遮挡环境下评估，通常不显式建模空间约束下的臂导航和手抓取控制需求。本文提出SpaceDex，一种分层框架，用于受限3D环境中的灵巧操作。高层通过视觉-语言模型解析用户意图，推断多视角中的遮挡和高度关系，生成零样本分割和掩码跟踪的目标边界框。该阶段为下游控制提供结构化空间指导，而非依赖单视角目标选择。底层引入臂手特征分离网络，解耦臂的全局轨迹控制与手的几何感知抓取模式选择，减少抓取与抓取目标间的特征干扰。控制器进一步整合多视角感知、指尖触觉传感和少量恢复示范，提高对部分可观测性和非预期接触的鲁棒性。在100次真实测试中涉及超过30个未见过的物体，覆盖四个类别，SpaceDex达到63.0%的成功率，相比强桌面基线39.0%。这些结果表明，结合分层空间规划与臂手表示解耦可提升受限环境中的灵巧抓取性能。
英文摘要：
Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes. Most existing methods are evaluated in relatively unoccluded settings and typically do not explicitly model the distinct control requirements of arm navigation and hand articulation under spatial constraints. We present SpaceDex, a hierarchical framework for dexterous manipulation in constrained 3D environments. At the high level, a Vision-Language Model (VLM) planner parses user intent, reasons about occlusion and height relations across multiple camera views, and generates target bounding boxes for zero-shot segmentation and mask tracking. This stage provides structured spatial guidance for downstream control instead of relying on single-view target selection. At the low level, we introduce an arm-hand Feature Separation Network that decouples global trajectory control for the arm from geometry-aware grasp mode selection for the hand, reducing feature interference between reaching and grasping objectives. The controller further integrates multi-view perception, fingertip tactile sensing, and a small set of recovery demonstrations to improve robustness to partial observability and off-nominal contacts. In 100 real-world trials involving over 30 unseen objects across four categories, SpaceDex achieves a 63.0\% success rate, compared with 39.0\% for a strong tabletop baseline. These results indicate that combining hierarchical spatial planning with arm-hand representation decoupling improves dexterous grasping performance in spatially constrained environments.
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。
