增强物理人机交互：通过机器人本体触觉感知识别数字
Enhancing Physical Human-Robot Interaction: Recognizing Digits via Intrinsic Robot Tactile Sensing
AI总结：
利用协作机器人内置扭矩传感器采集人手在触控板上书写数字时的关节力矩和末端力数据，通过双向LSTM网络实现94%准确率的在线数字识别，并在水果递送任务中验证其应用潜力。
摘要：
物理人机交互（pHRI）仍然是实现与机器人直观安全交互的关键挑战。当前的进展通常依赖外部触觉传感器作为接口，这增加了机器人系统的复杂性。在本研究中，我们利用协作机器人的本体触觉感知能力，识别用户在安装在机器人法兰上的无仪器触控板上绘制的数字。我们提出了一个数据集，包含机器人关节扭矩信号以及相应的末端执行器（EEF）力和力矩，这些数据来自机器人每个关节的集成扭矩传感器，用户在手写数字（0-9）时采集。pHRI-DIGI-TACT数据集从不同用户收集，以捕捉手写的自然变化。为增强分类鲁棒性，我们开发了一种数据增强技术来处理反转和旋转的数字输入。双向长短期记忆（Bi-LSTM）网络利用数据的时空特性，实现在线数字分类，在各种测试场景中总体准确率达到94%，包括涉及未参与系统训练的用户。该方法在真实机器人上的水果递送任务中实现，展示了其辅助日常生活的潜力。数据集和视频演示可在 https://TS-Robotics.github.io/pHRI-DIGI/ 获取。
英文摘要：
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: https://TS-Robotics.github.io/pHRI-DIGI/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
