用认知地图学习控制机械臂，无需逆运动学方程。
Robot Arm Control via Cognitive Map Learners
- 各关节独立训练认知地图模块，动态组合控制
- 通过相位超向量编码目标点，直接解算关节角
- 适用于2D任意节数和3D单旋转基座机械臂
认知地图学习器（CML）可实现分层、组合式的机器学习：独立训练的CML模块可任意组合解决更复杂问题，无需任务特定重训练。本文将该方法应用于多关节机械臂运动控制，每个臂段的转角由独立训练的CML决定。在二维笛卡尔平面中，目标点通过分数幂编码（FPE）转化为相位超向量，再通过谐振网络或现代霍普菲尔德网络分解为各臂段角度。这些角度输入对应臂段的CML，直接驱动机械臂到达目标点，无需使用逆运动学方程。本工作提出一种通用解法，适用于任意节数的2D机械臂，以及一种特定解法，适用于带单旋转基座的3D机械臂。
原文摘要 · Abstract (English)
Cognitive map learners (CML) have been shown to enable hierarchical, compositional machine learning. That is, interpedently trained CML modules can be arbitrarily composed together to solve more complex problems without task-specific retraining. This work applies this approach to control the movement of a multi-jointed robot arm, whereby each arm segment's angular position is governed by an independently trained CML. Operating in a 2D Cartesian plane, target points are encoded as phasor hypervectors according to fractional power encoding (FPE). This phasor hypervector is then factorized into a set of arm segment angles either via a resonator network or a modern Hopfield network. These arm segment angles are subsequently fed to their respective arm segment CMLs, which reposition the robot arm to the target point without the use of inverse kinematic equations. This work presents both a general solution for both a 2D robot arm with an arbitrary number of arm segments and a particular solution for a 3D arm with a single rotating base.
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