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.

翻译：本文研究了一种新型机器人肢体的设计、控制与学习方法，该肢体通过采用贝内特连杆机构实现运动生成，能够在单个四足机器人中实现爬行动物与哺乳动物形态之间的参数化重构。与当前主要关注平面连杆的研究不同，本研究深入探讨了采用过约束连杆作为肢体机构的方案。过约束连杆在高级运动学中具有坚实的理论基础，但在机器人应用中尚未得到充分探索。本研究展示了过约束机器人肢体（ORLs）的形态学优势——其能够转变为平面或球形肢体，并以最简单的贝内特连杆作为ORL实例进行说明。我们应用模型预测控制（MPC）模拟了一系列过约束运动任务，结果表明在考虑落脚点距离和速度时，其能量效率优于平面肢体。这些结果进一步通过强化学习（RL）优化的过约束运动策略得到验证。从进化生物学视角看，这些发现凸显了爬行动物与哺乳动物在肢体设计中的机制差异，并首次记录了ORLs在动态运动中超越平面肢体设计的实例。未来研究将集中于在机器人硬件中部署基于模型和学习的过约束运动技能，以弥合仿真与现实间的差距，从而开发受进化启发的、高能效的新型机器人肢体控制方法。