For tendon-driven multi-fingered robotic hands, ensuring grasp adaptability while minimizing the number of actuators needed to provide human-like functionality is a challenging problem. Inspired by the Pisa/IIT SoftHand, this paper introduces a 3D-printed, highly-underactuated, five-finger robotic hand named the Tactile SoftHand-A, which features only two actuators. The dual-tendon design allows for the active control of specific (distal or proximal interphalangeal) joints to adjust the hand's grasp gesture. We have also developed a new design of fully 3D-printed tactile sensor that requires no hand assembly and is printed directly as part of the robotic finger. This sensor is integrated into the fingertips and combined with the antagonistic tendon mechanism to develop a human-hand-guided tactile feedback grasping system. The system can actively mirror human hand gestures, adaptively stabilize grasp gestures upon contact, and adjust grasp gestures to prevent object movement after detecting slippage. Finally, we designed four different experiments to evaluate the novel fingers coupled with the antagonistic mechanism for controlling the robotic hand's gestures, adaptive grasping ability, and human-hand-guided tactile feedback grasping capability. The experimental results demonstrate that the Tactile SoftHand-A can adaptively grasp objects of a wide range of shapes and automatically adjust its gripping gestures upon detecting contact and slippage. Overall, this study points the way towards a class of low-cost, accessible, 3D-printable, underactuated human-like robotic hands, and we openly release the designs to facilitate others to build upon this work. This work is Open-sourced at github.com/SoutheastWind/Tactile_SoftHand_A

翻译：对于肌腱驱动的多指机器人手而言，在最小化所需执行器数量的同时确保抓取适应性，以提供类人功能，是一个具有挑战性的问题。受Pisa/IIT SoftHand的启发，本文介绍了一种名为Tactile SoftHand-A的3D打印、高度欠驱动、五指机器人手，其仅配备两个执行器。其双肌腱设计允许主动控制特定（远端或近端指间）关节，以调整手的抓取姿态。我们还开发了一种全新的全3D打印触觉传感器设计，无需手工组装，可直接作为机器人手指的一部分打印而成。该传感器集成于指尖，并与拮抗肌腱机构相结合，开发出一套人手引导的触觉反馈抓取系统。该系统能够主动模仿人手姿态，在接触时自适应地稳定抓取姿态，并在检测到滑动后调整抓取姿态以防止物体移动。最后，我们设计了四项不同的实验，以评估结合了拮抗机构的新型手指在控制机器人手势态、自适应抓取能力以及人手引导的触觉反馈抓取能力方面的表现。实验结果表明，Tactile SoftHand-A能够自适应地抓取各种形状的物体，并在检测到接触和滑动时自动调整其抓握姿态。总体而言，本研究为一类低成本、易获取、可3D打印、欠驱动的类人机器人手指明了方向，我们公开了设计以促进他人在此基础上继续研究。本工作已在github.com/SoutheastWind/Tactile_SoftHand_A开源。