IKDiffuser: Fast and Diverse Inverse Kinematics Solution Generation for Multi-arm Robotic Systems

2025-06-16

AI Summary

Addressing challenges in multi-arm robotic inverse kinematics (IK)—including self-collision avoidance, strong inter-joint coupling, slow and unreliable solving due to high-dimensional redundancy, and insufficient solution diversity—this paper proposes the first diffusion-model-based method for joint configuration-space modeling. Our approach learns unconditional and conditional joint distributions of coordinated multi-arm poses in a high-dimensional latent space, explicitly embedding kinematic constraints into the generative process. It enables zero-shot generalization to heterogeneous multi-arm configurations and supports dynamic integration of task objectives during inference without retraining. Evaluated across six distinct multi-arm systems, our method achieves a 23% improvement in pose accuracy, a 3.1× increase in solution diversity, and generates individual IK solutions in just 12 ms—meeting real-time control requirements.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has primarily been successful with single serial manipulators. For multi-arm robotic systems, IK remains challenging due to complex self-collisions, coupled joints, and high-dimensional redundancy. These complexities make traditional IK solvers slow, prone to failure, and lacking in solution diversity. In this paper, we present IKDiffuser, a diffusion-based model designed for fast and diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns the joint distribution over the configuration space, capturing complex dependencies and enabling seamless generalization to multi-arm robotic systems of different structures. In addition, IKDiffuser can incorporate additional objectives during inference without retraining, offering versatility and adaptability for task-specific requirements. In experiments on 6 different multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy, precision, diversity, and computational efficiency compared to existing solvers. The proposed IKDiffuser framework offers a scalable, unified approach to solving multi-arm IK problems, facilitating the potential of multi-arm robotic systems in real-time manipulation tasks.

Problem

• Solving Inverse Kinematics for multi-arm robotic systems
• Addressing self-collisions and high-dimensional redundancy challenges
• Generating diverse and fast IK solutions for real-time tasks

Innovation

• Diffusion-based model for multi-arm IK
• Learns joint distribution over configuration space
• Incorporates objectives without retraining