Whole-Body Inverse Kinematics with Graph Diffusion
This work addresses the challenge of generalizable and multi-modal inverse kinematics for robots with complex kinematic structures, which is critical for robotic manipulation and motion planning.
GraphDiff-IK introduces a graph diffusion framework for whole-body inverse kinematics that directly generates joint configurations on a kinematic graph derived from the robot URDF. The method achieves accurate and stable IK performance across diverse robot morphologies, including single-arm, dual-arm, and articulated robots with torso structures, while preserving multi-modal solution generation.
Inverse kinematics (IK) is a fundamental problem in robotics, requiring the generation of joint configurations that satisfy target end-effector poses. Existing approaches often struggle to generalize across diverse robot morphologies and to effectively model the multi-modal nature of IK, particularly in articulated systems with multiple kinematic branches. In this work, we propose GraphDiff-IK, a structure-aware graph diffusion framework for inverse kinematics. Specifically, we represent the robot as a kinematic graph constructed from the robot URDF, where nodes correspond to actuated joints and edges encode kinematic dependencies. Building upon this representation, we formulate IK as a conditional graph diffusion process that directly generates joint configurations on the robot graph. To better capture structural dependencies in articulated systems, we further introduce a structure-aware graph reasoning framework with hierarchical stage-wise message passing and torso-aware conditioning for multi-branch robots. In addition, we incorporate noisy forward kinematics feedback and task-space supervision to improve geometric consistency during denoising. The proposed framework provides a unified formulation that naturally supports single-arm robots, dual-arm systems, and articulated robots with torso or waist structures. Extensive experiments on diverse robotic platforms demonstrate that the proposed method achieves accurate and stable IK performance while preserving the ability to generate multiple feasible solutions for redundant robotic systems.