IKDP: Inverse Kinematics through Diffusion Process
On the map: Diffusion models → Diffusion and flow policies for robotics
Abstract (arXiv)
It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some people have tried to find the answer by machine learning. In this project, we will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK. Index Terms: Inverse kinematics, Denoising Diffusion Probabilistic Model, self Attention, Transformer
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 2 of its 3 references that are in the map. First posted on arXiv 20 Oct 2024.
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- 2025IKDiffuser: a Diffusion-based Generative Inverse Kinematics Solver for Kinematic Trees
- 2023Efficient Learning of Fast Inverse Kinematics with Collision Avoidance
- 2026Exploring the Intrinsic Geometry of Diffusion Models with Constrained Inverse Kinematics
- 2024Denoising Diffusion Planner: Learning Complex Paths from Low-Quality Demonstrations
- 2023Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
- 2025Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation
- 2022Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
- 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models