  This paper studies the design, control, and learning of a novel robotic limb
that produces overconstrained locomotion by employing the Bennett linkage for
motion generation, capable of parametric reconfiguration between a reptile- and
mammal-inspired morphology within a single quadruped. In contrast to the
prevailing focus on planar linkages, this research delves into adopting
overconstrained linkages as the limb mechanism. The overconstrained linkages
have solid theoretical foundations in advanced kinematics but are
under-explored in robotic applications. This study showcases the morphological
superiority of Overconstrained Robotic Limbs (ORLs) that can transform into
planar or spherical limbs, exemplified using the simplest case of a Bennett
linkage as an ORL. We apply Model Predictive Control (MPC) to simulate a range
of overconstrained locomotion tasks, revealing its superiority in energy
efficiency against planar limbs when considering foothold distances and speeds.
The results are further verified in overconstrained locomotion policies
optimized from Reinforcement Learning (RL). From an evolutionary biology
perspective, these findings highlight the mechanism distinctions in limb design
between reptiles and mammals and represent the first documented instance of
ORLs outperforming planar limb designs in dynamic locomotion. Future studies
will focus on deploying the model-based and learning-based overconstrained
locomotion skills in the robotic hardware to close the Sim2Real gap for
developing evolutionary-inspired, energy-efficient control of novel robotic
limbs.
