发表机构
University of Hamburg（汉堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MorphIK提出一种基于流匹配和Transformer的形态条件神经逆运动学模型，能在未见机器人上实现约5厘米精度，并作为先验经优化降至亚毫米级，同时支持零空间多样化采样。
AI 中文摘要
神经模型可以从数据中学习生成逆运动学问题的多种解决方案，但通常仅限于单个机器人。我们提出了MorphIK，一种流匹配模型，能够为训练中从未见过的基于旋转关节的运动链求解逆运动学。该模型采用Transformer架构来编码机器人的形态以及目标位姿。这种编码随后条件化一个流匹配头，从噪声中生成位姿。在程序化生成机器人的纯合成数据上训练后，该模型在具有6至9自由度的未见真实世界机器人上达到了约5厘米的精度。为了获得更高精度，该模型可作为进一步优化算法的优秀先验，在单步阻尼最小二乘优化后将误差降至小于1厘米，在大多数情况下经过3步后误差降至亚毫米级。基于流匹配生成能力产生高度多样化输出的优势，我们的模型能够高效采样机器人的零空间，为同一姿态提供多种多样的配置。因此，总体而言，MorphIK允许针对众多已知和未知机器人学习和泛化神经逆运动学。
英文摘要
Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We present MorphIK, a flow-matching model that solves inverse kinematics for revolute-joint-based kinematic chains it has never seen during training. The model uses a transformer architecture to encode the robot's morphology along with the target pose. This encoding then conditions a flow-matching head that generates poses from noise. Trained on purely synthetic data from procedurally generated robots, the model reaches a precision of about 5 cm on unseen real-world robots with 6 to 9 Degrees of Freedom. For higher precision, the model serves as an excellent Prior for further optimization algorithms, reducing error to less than 1 cm after a single step of Damped Least Squares optimization and to sub-1 mm error after 3 steps in most cases. Building on flow matching's generative capabilities to produce highly diverse outputs, our model can efficiently sample the robot's null space, providing a wide variety of configurations for the same pose. Thus, overall, MorphIK allows learning and generalizing neural inverse kinematics for a multitude of known and unknown robots.
