LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models

arXiv:2609.21275 · cs.RO · Submitted 2026-09-18 · Read on arXiv

cs.RO

Submitted: 2026-09-18

Updated: 2026-09-18

Terminology

• Latent Space — 12× in this paper · explained in 7 episodes
• Inverse Problem — 9× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• International Conference on Machine Learning — 2× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes

Sources

• Whole-Body Inverse Kinematics with Graph Diffusion
• SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models

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