Tactile information effectively enables faster training and better task performance for learning-based in-hand manipulation. Existing approaches are validated in simulated environments with a large number of tactile sensors. However, attaching such sensors to a real robot hand is not applicable due to high cost and physical limitations. To enable real-world adoption of tactile sensors, this study investigates the impact of tactile sensors, including their varying quantities and placements on robot hands, on the dexterous manipulation task performance and analyzes the importance of each. Through empirically decreasing the sensor quantities, we successfully find an optimized set of tactile sensors (21 sensors) configuration, which keeps over 93% task performance with only 20% sensor quantities compared to the original set (92 sensors) for the block manipulation task, leading to a potential reduction of over 80% in sensor manufacturing and design costs. To transform the empirical results into a generalizable understanding, we build a task performance prediction model with a weighted linear regression algorithm and use it to forecast the task performance with different sensor configurations. To show its generalizability, we verified this model in egg and pen manipulation tasks and achieved an average prediction error of 3.12%.

翻译：触觉信息能有效促进基于学习的灵巧手操作任务的训练速度与性能提升。现有方法通常在配备大量触觉传感器的仿真环境中验证。然而，由于高昂成本与物理限制，在真实机器人手上部署如此数量的传感器并不现实。为推动触觉传感器在真实场景中的应用，本研究系统探究了触觉传感器（包括其数量变化与在机械手上的布局方式）对灵巧操作任务性能的影响，并分析了各传感器的重要性。通过实证减少传感器数量，我们成功找到一组优化的触觉传感器配置（21个传感器），在方块操作任务中仅使用原配置（92个传感器）20%的传感器数量，即可保持超过93%的任务性能，有望降低超过80%的传感器制造与设计成本。为将实证结果转化为可推广的认知，我们采用加权线性回归算法构建了任务性能预测模型，并利用该模型预测不同传感器配置下的任务表现。为验证其泛化能力，我们在鸡蛋与钢笔操作任务中对该模型进行测试，取得了平均3.12%的预测误差。