Adaptive movement sequences and predictive decisions based on hierarchical dynamical systems

This paper addresses the question of how to create adaptive and smooth sequences of actions and how to decide among skill options in a continuous manner without the necessity of recurrent planning. Motion generation is based on serial and parallel blending of movement primitives (MP). MPs are modeled as dynamical systems on task coordinates with attractor behavior and augmented with additional signals to ease their coordination. Sequences and transitions between skills are realized in a unified way as bifurcating dynamical systems based on continuous-time recurrent neural networks. The neural output is used as activation signal for MPs. Besides continuous feedback from the controlled MPs, the neural dynamics is influenced by a cost term from a future prediction to allow the inhibition of an action flow that is expected to fail. First results are shown in a physical simulation environment on a high-DoF robotic hand-arm system. The system is capable of creating smooth transients of MPs. Robustness to disturbances can be observed as local adaptations of individual low-level MPs, flexible sequencing of MPs, and global error recovery by changing the whole strategy of how to perform a movement skill.