发表机构
The University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LOInK通过结构化神经代理模型学习配置空间到解耦任务/潜在空间的双Lipschitz映射，将成本最小化解置于原点，利用算子分裂实现高效采样，在机械臂、四足攀爬机器人和软体执行器上验证了其快速生成近最优解的能力。
AI 中文摘要
我们提出了学习最优逆运动学（LOInK），一种为逆运动学问题生成近似最优解的方法。当在由采样配置、相关任务变量和给定成本函数组成的数据上训练时，LOInK学习一个从配置空间到解耦的任务/潜在空间的双Lipschitz可逆映射，而且，潜在空间被结构化，使得成本最小化解位于原点。这通过基于算子分裂的网络反演算法实现了成本最小化解的高效采样。我们在三个问题上演示了所提出的方法：一个说明性的三自由度机械臂问题；一个四足攀爬机器人，对于该机器人，LOInK平均能比约束优化方法快31倍，最快快100倍地生成近最优解；以及一个模拟软体执行器作为纯数据驱动的例子，其中LOInK能明确生成高质量解，而现有的生成方法需要多样化的采样和候选解的评估。
英文摘要
We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of sampled configurations and associated task variables and a given cost function, LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, and moreover, the latent space is structured so as to place cost-minimizing solutions at the origin. This enables efficient sampling of cost-minimizing solutions via a network-inversion algorithm based on operator splitting. We demonstrate the proposed approach on three problems: an illustrative three degree-of-freedom manipulator problem; a quadrupedal climbing robot for which LOInK can generate near-optimal solutions on average 31 times faster and up to 100 times faster than a constrained optimization approach; and a simulated soft actuator as a purely data-driven example, in which LOInK can explicitly generate high-quality solutions, unlike existing generative approaches that require diverse sampling and evaluation of candidate solutions.
