发表机构
University of Southern California; University of New Mexico（南加州大学; 新墨西哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过真实世界实验比较视觉、力矩和触觉反馈，发现组合交互信号显著提升杂乱灵巧抓取成功率，并揭示视觉-交互差距。
AI 中文摘要
杂乱环境中的灵巧抓取提出了一个基本的感知问题：触觉测量和外部力矩估计何时能改进视觉几何？遮挡和接触可能模糊抓取质量，促使对这些交互信号进行受控评估。我们在一套灵巧系统上进行了五项桌面场景条件的受控真实世界研究，该系统结合了视觉、每指和手腕力矩估计以及分布式指尖触觉传感器。在演示、视觉观察、动作空间和柔顺控制固定的情况下，我们比较了仅视觉、力矩、触觉和组合策略以及表示和融合基线。组合策略在24/25次试验中成功，而仅视觉为14/25；在三个受限条件下，组合策略为15/15，而仅视觉为6/15。消融实验表明，力矩和触觉反馈是互补的。行为比较表明，交互反馈能够更早地拒绝不充分的接触，在提升前重新抓取，并实现更稳定的抓取。据我们所知，这是首个在杂乱环境中针对目标导向灵巧抓取，结合并分别评估这些交互模态的真实世界研究。这些结果描绘了不断扩大的视觉-交互差距，并将杂乱灵巧抓取定位为确定学习策略何时需要交互感知的基准。项目网站：此https URL
英文摘要
Dexterous grasping in clutter poses a basic sensing question: when do tactile measurements and external wrench estimates improve on visual geometry? Occlusion and contact can obscure grasp quality, motivating a controlled evaluation of these interaction signals. We present a controlled real-world study over five tabletop scene conditions on a dexterous system that combines vision, per-finger and wrist wrench estimates, and distributed fingertip taxels. With demonstrations, visual observations, action space, and compliant control fixed, we compare vision-only, wrench, taxel, and combined policies plus representation and fusion baselines. The combined policy succeeds in 24/25 trials versus 14/25 for vision only, and 15/15 versus 6/15 across the three confined conditions. Ablations show that wrench and taxel feedback are complementary. Behavioral comparisons show that interaction feedback enables earlier rejection of inadequate contacts, regrasping before lift, and more stable grasps. To our knowledge, this is the first real-world study to combine and separately evaluate these interaction modalities for target-oriented dexterous grasping in clutter. These results chart a widening vision-interaction gap and position cluttered dexterous grasping as a benchmark for determining when the learned policy needs interaction sensing. Project website: https://interaction-dex-grasp.github.io/
Comments9 pages, 7 figures, 2 tables. Project website: https://interaction-dex-grasp.github.io/
