Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.

翻译：触觉传感器正进入机器人领域，为接触表面提供直接信息，包括接触事件、滑动事件乃至纹理识别。这些事件对于机器人手设计（包括假肢）尤为重要，因为它们能显著提升抓握稳定性。然而，目前大多数已发布的机器人手设计在手掌表面采用差异巨大的密度与布局配置触觉传感器，常占据可用空间的主要部分。本研究通过仿真评估了六种不同密度与布局的触觉传感器配置，基于其对强化学习的影响进行对比分析。我们采用的双实验平台系统确保了研究结果的稳健性，其结论不依赖于特定物理模拟器、机器人手模型或机器学习算法。实验结果表明，六种传感器配置的仿真既存在平台特异性影响，也呈现跨平台的普遍规律；我们识别出一种在双实验平台中均能持续获得最佳性能的配置方案。这些发现可为未来机器人手（包括假肢）的设计研究提供参考。