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 robots with motors placed near the joints. 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 wear and 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：实现机器人高速且精确的操作一直是机器人研究的长期目标。平衡这些相互竞争的需求是实现机器人与人类无缝协作并提高任务性能的关键。然而，传统的电机驱动系统往往难以在此平衡中取得理想效果。由于其刚性且通常笨重的设计（将电机置于关节中加剧了这一问题），此类机器人的快速运动会在碰撞时传递巨大的冲击力。为了实现精确且安全的动态运动，我们提出了一种四自由度肌腱驱动机器人手臂。肌腱传动允许将驱动装置置于基座，从而降低机器人的惯性；我们证明，与将电机安装在关节附近的传统机器人相比，该设计能显著降低峰值碰撞力。将我们的机器人与气动肌肉配对使用，可以产生高驱动力和高度加速的运动，同时通过被动柔顺性获得抗冲击能力。由于肌腱存在额外的摩擦，因而容易磨损，我们通过包括长期动态运动在内的多种实验验证了该机器人手臂的可靠性。我们还通过量化系统的非线性特性，以及展示其在具有挑战性的动态乒乓球任务上的性能（该任务通过强化学习从零开始习得），证明了其易于控制的特点。我们在 webdav.tuebingen.mpg.de/pamy2 开源了整个硬件设计（大部分部件可3D打印）、控制软件以及一个包含25天多样化机器人运动的本体感知数据集。