对比6种触觉传感器布局，发现一种配置在模拟中提升抓握学习效率最佳。
The impact of tactile sensor configurations on grasp learning efficiency -- a comparative evaluation in simulation
- 在仿真中测试6种触觉传感器密度与布局对强化学习的影响。
- 一种特定布局在两种设置下均表现最优，提升抓握稳定性。
- 结果可指导假肢等机器人手的设计，减少实验试错成本。
触觉传感器正被引入机器人领域，以提供接触事件、滑移事件甚至纹理识别等直接信息，对提升机械手（包括假肢）的抓握稳定性至关重要。然而，现有机器人手设计中，触觉传感器的分布密度和布局差异极大，常占据大量可用空间。本文通过仿真评估了6种不同密度与布局的触觉传感器配置对强化学习抓握效率的影响。采用双设置系统，确保结果不受特定物理引擎、机械手模型或机器学习算法依赖。实验结果显示，各配置在特定场景及跨场景中均表现出显著差异，其中一种布局在所有测试中持续表现最佳。该发现可为未来机器人手设计，特别是假肢研发，提供重要参考。
原文摘要 · Abstract (English)
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.
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