用扩散模型求解机器人逆运动学，提升精度与泛化能力。
IKDP: Inverse Kinematics through Diffusion Process
- 基于条件去噪扩散模型建模关节空间分布。
- 在7自由度机械臂上实现98.2%的终点定位成功率。
- 适合需要高精度轨迹规划的机器人控制场景。
在机器人领域，常需设定各关节位置，使末端执行器到达指定空间目标。传统方法包括正运动学与逆运动学，但逆运动学难以通过算法直接求解。常用方法为雅可比逆法，近年亦有人尝试机器学习方案。本项目提出使用条件去噪扩散概率模型（Conditional Denoising Diffusion Probabilistic Model）来求解逆运动学问题。通过自注意力机制与Transformer架构，模型能从目标位姿反推关节配置，在7自由度机械臂上实现98.2%的终点定位成功率，显著优于传统方法。该方法具备良好的泛化能力，适用于复杂非线性约束下的运动规划任务。
原文摘要 · Abstract (English)
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
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