 To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions . Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
<coherence>  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions . Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
 To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions , and (iii) integrate a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.