aiqbench analysis
LOInK represents a significant advancement in machine learning for robotics by using structured neural networks to solve inverse kinematics problems orders of magnitude faster than traditional constrained optimization methods. This approach enables efficient sampling of cost-minimizing solutions for complex systems like quadrupedal climbers and soft actuators through a novel network-inversion algorithm. The work demonstrates how data-driven techniques can explicitly generate high-quality motion plans without relying on extensive candidate evaluation.
LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
arXiv:2609.21275v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.21275 [cs.RO]
(or arXiv:2609.21275v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.21275 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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