IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model
This addresses the problem of slow and failure-prone IK solvers for multi-arm robots, enabling real-time manipulation tasks, though it appears incremental as it applies a known method (diffusion models) to a specific domain.
The paper tackled the challenge of solving Inverse Kinematics (IK) for multi-arm robotic systems, which is difficult due to self-collisions and high-dimensional redundancy, and presented IKDiffuser, a diffusion-based model that achieved superior accuracy, precision, diversity, and computational efficiency in experiments on 6 different systems.
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.