  While the animals' Fin-to-Limb evolution has been well-researched in biology,
such morphological transformation remains under-adopted in the modern design of
advanced robotic limbs. This paper investigates a novel class of
overconstrained locomotion from a design and learning perspective inspired by
evolutionary morphology, aiming to integrate the concept of `intelligent design
under constraints' - hereafter referred to as constraint-driven design
intelligence - in developing modern robotic limbs with superior energy
efficiency. We propose a 3D-printable design of robotic limbs parametrically
reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett
linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation,
identical to the modern legged robot platforms, with the added capability of
upgrading into a wheel-legged system. Then, we implemented a large-scale,
multi-terrain deep reinforcement learning framework to train these
reconfigurable limbs for a comparative analysis of overconstrained locomotion
in energy efficiency. Results show that the overconstrained limbs exhibit more
efficient locomotion than planar limbs during forward and sideways walking over
different terrains, including floors, slopes, and stairs, with or without
random noises, by saving at least 22% mechanical energy in completing the
traverse task, with the spherical limbs being the least efficient. It also
achieves the highest average speed of 0.85 meters per second on flat terrain,
which is 20% faster than the planar limbs. This study paves the path for an
exciting direction for future research in overconstrained robotics leveraging
evolutionary morphology and reconfigurable mechanism intelligence when combined
with state-of-the-art methods in deep reinforcement learning.
