  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.
