过约束运动
Overconstrained Locomotion
AI总结：
本文提出一种基于 Bennett linkage 的过约束机械肢（ORLs），能在单个四足机器人内参数化重构为爬行或哺乳动物形态。通过 MPC 和 RL 验证其在能效上优于平面肢体，为进化启发的机器人肢体设计提供了首个实例。
摘要：
本文研究了一种新型机械肢的设计、控制与学习，该机械肢通过采用 Bennett linkage 进行运动生成，产生过约束运动，能够在单个四足机器人在基于爬行动物和哺乳动物启发的形态之间进行参数化重构。与当前对平面连杆机构的关注不同，本研究深入探讨了采用过约束连杆机构作为肢体机构。过约束连杆机构在高级运动学中具有坚实的理论基础，但在机器人应用中探索不足。本研究展示了过约束机械肢（ORLs）的形态优越性，ORLs 可以转变为平面或球形肢体，并以 Bennett linkage 作为 ORL 的最简单情况为例进行说明。我们应用模型预测控制（MPC）模拟了一系列过约束运动任务，揭示了在考虑落脚点距离和速度时，其在能效方面相对于平面肢体的优越性。结果在通过强化学习（RL）优化的过约束运动策略中得到了进一步验证。从进化生物学的角度来看，这些发现突出了爬行动物和哺乳动物在肢体设计上的机制差异，并代表了 ORLs 在动态运动中优于平面肢体设计的首个记录实例。未来的研究将重点在机器人硬件上部署基于模型和基于学习的过约束运动技能，以弥合 Sim2Real 差距，从而开发受进化启发的、能效高的新型机械肢控制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
