Brachiation is a dynamic, coordinated swinging maneuver of body and arms used by monkeys and apes to move between branches. As a unique underactuated mode of locomotion, it is interesting to study from a robotics perspective since it can broaden the deployment scenarios for humanoids and animaloids. While several brachiating robots of varying complexity have been proposed in the past, this paper presents the simplest possible prototype of a brachiation robot, using only a single actuator and unactuated grippers. The novel passive gripper design allows it to snap on and release from monkey bars, while guaranteeing well defined start and end poses of the swing. The brachiation behavior is realized in three different ways, using trajectory optimization via direct collocation and stabilization by a model-based time-varying linear quadratic regulator (TVLQR) or model-free proportional derivative (PD) control, as well as by a reinforcement learning (RL) based control policy. The three control schemes are compared in terms of robustness to disturbances, mass uncertainty, and energy consumption. The system design and controllers have been open-sourced. Due to its minimal and open design, the system can serve as a canonical underactuated platform for education and research.

翻译：臂行是猴类和猿类在树枝间移动时身体与手臂进行的动态协调摆动动作。作为一种独特的欠驱动运动模式，从机器人学视角研究其能够扩展人形与动物形机器人的部署场景，因而具有重要价值。尽管以往已提出多种不同复杂度的臂行机器人，本文展示了一种最简原型——仅使用单个执行器与非驱动夹爪。新型被动夹爪设计使其能够卡入并释放单杠，同时保证摆动起始与结束位姿的明确界定。通过三种方式实现臂行行为：采用直接配点法进行轨迹优化，并利用基于模型的时变线性二次型调节器（TVLQR）或无模型比例-微分（PD）控制实现稳定化，以及基于强化学习（RL）的控制策略。从抗干扰鲁棒性、质量不确定性与能耗三个方面对三种控制方案进行比较。系统设计与控制器已开源。凭借其极简与开放的设计，该系统可作为经典的欠驱动平台用于教育与研究。