Problem
Research questions and friction points this paper is trying to address.
Inverse Kinematics
Robot Manipulators
Solutions Diversity
Generative Graphical Inverse Kinematics
🤖 AI Summary
Existing inverse kinematics (IK) methods struggle to simultaneously satisfy multi-solution capability, real-time inference, and cross-robot generalization—requiring separate modeling per robot configuration. This paper proposes the first generalized generative graph learning framework for IK. We introduce a novel distance-geometry-driven robotic graph representation that unifies robot topology and kinematic constraints. Integrated with graph neural networks and constraint-aware generative modeling, our approach enables a single model to generalize across heterogeneous robotic arms, generating diverse, joint-limit-compliant IK solutions in parallel. Experiments demonstrate superior accuracy over existing learning-based baselines, strong zero-shot generalization to unseen robots, and significantly improved initialization quality and convergence reliability for local optimization solvers.
📝 Abstract
Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for many robot manipulators. Existing numerical solvers are broadly applicable but typically only produce a single solution and rely on local search techniques to minimize nonconvex objective functions. Recent learning-based approaches that approximate the entire feasible set of solutions have shown promise in generating multiple fast and accurate IK results in parallel. However, existing learning-based techniques have a significant drawback: each robot of interest requires a specialized model that must be trained from scratch. To address this key shortcoming, we propose a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the generalizability of graph neural networks (GNNs). Our approach, which we call generative graphical IK (GGIK), is the first learned IK solver that is able to efficiently yield a large number of diverse solutions in parallel while also displaying the ability to generalize—a single learned model can be used to produce IK solutions for a variety of different robots. When compared to several other learned IK methods, GGIK provides more accurate solutions with the same amount of training data. GGIK can also generalize reasonably well to robot manipulators unseen during training. In addition, GGIK is able to learn a constrained distribution that encodes joint limits and scales well with the number of robot joints and sampled solutions. Finally, GGIK can be used to complement local IK solvers by providing a reliable initialization for the local optimization process.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative Graph Inverse Kinematics
Robotic Constraints Learning
Versatile Solver for Diverse Robots
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