Tactile sensing is critical for learning-based dexterous manipulation, yet principled guidelines for sensor placement remain largely absent. While dense sensor arrays provide rich contact feedback, they impose significant hardware costs and can even degrade policy performance by introducing redundant or conflicting inputs. This paper presents the first systematic framework for quantifying the contribution of individual tactile sensors to deep reinforcement learning (DRL) policy performance. We propose a two-stage approach: a coarse empirical pruning phase that reduces the sensor count on the Shadow Hand from 92 to 21 while retaining 93\% task performance, followed by a fine-grained active learning phase that combines Gaussian Process Regression (GPR) with Lasso regression to rank the functional importance of each remaining sensor. Our analysis reveals that sensors on the thumb, ring finger, and little finger dominate manipulation performance, while middle-finger sensors exhibit negative contributions -- actively degrading policy learning. Ablation studies across three manipulation tasks (block, egg, and pen) confirm that a 14-sensor configuration preserves over 90\% of the full-array performance. Zero-shot transfer experiments on two novel objects and cross-platform validation on the Allegro and Leap Hand further demonstrate that the identified importance rankings generalize across tasks and robot morphologies. These findings establish quantitative deployment guidelines that enable practitioners to select cost-effective sensor configurations with predictable performance trade-offs.

翻译：触觉感知对于基于学习的灵巧操作至关重要，然而关于传感器放置的原则性指导方针仍基本缺失。虽然密集传感器阵列提供了丰富的接触反馈，但会带来显著的硬件成本，甚至可能因引入冗余或冲突的输入而降低策略性能。本文提出了首个用于量化单个触觉传感器对深度强化学习策略性能贡献的系统性框架。我们提出了一种两阶段方法：首先是粗粒度经验剪枝阶段，将Shadow Hand的传感器数量从92个减少到21个，同时保留93%的任务性能；随后是细粒度主动学习阶段，结合高斯过程回归与Lasso回归，对每个剩余传感器的功能重要性进行排序。我们的分析揭示，拇指、无名指和小指上的传感器主导着操作性能，而中指传感器则表现出负贡献——主动削弱策略学习。在三个操作任务（方块、鸡蛋和笔）上的消融研究证实，14个传感器的配置能保留全阵列90%以上的性能。在两个新物体上的零样本迁移实验以及在Allegro和Leap Hand上的跨平台验证进一步表明，识别出的重要性排序可泛化至不同任务和机器人形态。这些发现建立了量化部署指南，使从业者能选择具有可预测性能权衡的成本效益型传感器配置。