Operating robots precisely and at high speeds has been a long-standing goal of robotics research.Balancing these competing demands is key to enabling the seamless collaboration of robots and humans and increasing task performance. However, traditional motor-driven systems often fall short in this balancing act.Due to their rigid and often heavy design exacerbated by positioning the motors into the joints, faster motions of such robots transfer high forces at impact. To enable precise and safe dynamic motions, we introduce a four degree-of-freedom~(DoF) tendon-driven robot arm. Tendons allow placing the actuation at the base to reduce the robot's inertia, which we show significantly reduces peak collision forces compared to conventional motor-driven systems.Pairing our robot with pneumatic muscles allows generating high forces and highly accelerated motions, while benefiting from impact resilience through passive compliance. Since tendons are subject to additional friction and hence prone to tear, we validate the reliability of our robotic arm on various experiments, including long-term dynamic motions. We also demonstrate its ease of control by quantifying the nonlinearities of the system and the performance on a challenging dynamic table tennis task learned from scratch using reinforcement learning. We open-source the entire hardware design, which can be largely 3D printed, the control software, and a proprioceptive dataset of 25 days of diverse robot motions at webdav.tuebingen.mpg.de/pamy2/.

翻译：长期以来，机器人研究的一个核心目标是实现机器人的精准与高速操作。平衡这些相互竞争的需求是实现机器人与人类无缝协作、提升任务性能的关键。然而，传统电机驱动系统往往难以胜任这一平衡任务。由于电机通常位于关节处而导致的刚体及沉重设计，此类机器人在快速运动中会产生巨大的碰撞冲击力。为实现精准且安全的动态运动，我们提出了一种四自由度肌腱驱动机器人臂。肌腱结构可将驱动器置于基座以降低机器人惯性，实验证明其峰值碰撞力显著低于传统电机驱动系统。通过将机器人臂与气动肌肉结合，我们能够产生高驱动力与高加速度运动，同时利用被动柔顺性实现抗冲击能力。鉴于肌腱易受额外摩擦影响而存在撕裂风险，我们通过长期动态运动等多项实验验证了机器人臂的可靠性。通过量化系统非线性特性及在强化学习从零训练的具挑战性动态乒乓球任务上的表现，我们证明了其易控性。我们在webdav.tuebingen.mpg.de/pamy2/上开源了全套硬件设计（可主要采用3D打印实现）、控制软件及包含25天多样化机器人运动的本体感知数据集。