LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models

2026-09-17

AI Summary

提出LOInK方法，通过结构化神经代理模型学习逆运动学的近似最优解，有效生成成本最小化解决方案。

Abstract

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.

Problem

• Inverse Kinematics
• Optimal Solutions
• Efficiency
• Robotics
• Soft Actuators

Innovation

• Learned Optimal Inverse Kinematics
• bi-Lipschitz invertible mapping
• operator splitting
• cost-minimizing solutions
• neural surrogate models