Abstract
Physical human-robot interaction (pHRI) remains a key challenge for achieving intuitive and safe interaction with robots. Current advancements often rely on external tactile sensors as interface, which increase the complexity of robotic systems. In this study, we leverage the intrinsic tactile sensing capabilities of collaborative robots to recognize digits drawn by humans on an uninstrumented touchpad mounted to the robot's flange. We propose a dataset of robot joint torque signals along with corresponding end-effector (EEF) forces and moments, captured from the robot's integrated torque sensors in each joint, as users draw handwritten digits (0-9) on the touchpad. The pHRI-DIGI-TACT dataset was collected from different users to capture natural variations in handwriting. To enhance classification robustness, we developed a data augmentation technique to account for reversed and rotated digits inputs. A Bidirectional Long Short-Term Memory (Bi-LSTM) network, leveraging the spatiotemporal nature of the data, performs online digit classification with an overall accuracy of 94\% across various test scenarios, including those involving users who did not participate in training the system. This methodology is implemented on a real robot in a fruit delivery task, demonstrating its potential to assist individuals in everyday life. Dataset and video demonstrations are available at: this https URL.
Abstract (translated)
物理人机交互（pHRI）仍然是实现直观和安全的人机互动的关键挑战。当前的进展通常依赖于外部触觉传感器作为接口，这增加了机器人的复杂性。在这项研究中，我们利用协作机器人本身的内在触觉感应能力来识别人类在未配备任何仪器的安装于机械臂法兰上的触摸板上书写的数字（0-9）。我们提出了一组数据集，该数据集包括从每个关节集成扭矩传感器捕获的机器人的关节扭矩信号以及相应的末端执行器（EEF）力和力矩。该pHRI-DIGI-TACT数据集来自不同的用户，以捕捉手写风格中的自然变化。为了增强分类的鲁棒性，我们开发了一种数据增强技术来应对倒置和旋转数字输入的情况。 采用双向长短期记忆（Bi-LSTM）网络利用数据的空间时间和序列特性，在各种测试场景中实现了在线数字识别，整体准确率达到了94%，包括那些未参与训练系统用户的测试。该方法在水果配送任务上通过真实机器人进行了实施，展示了其在未来日常生活中帮助个人的潜力。 数据集和视频演示可以在以下网址找到：this https URL.
URL
https://arxiv.org/abs/2504.00167
