Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion

TL;DR

This paper presents a tendon-driven robot arm that achieves safe, precise, and high-speed dynamic motions by reducing inertia and impact forces, enabling advanced tasks like table tennis through reinforcement learning.

Contribution

Introduction of a lightweight, tendon-driven robot arm with base-mounted actuation and pneumatic muscles, enhancing safety, speed, and control in dynamic robotic tasks.

Findings

Significantly reduced peak collision forces compared to traditional robots.

Successful learning and execution of a dynamic table tennis task.

Validated long-term reliability and impact resilience of the robot.

Abstract

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.