The neuronal basis of movement preparation, during which movement parameters such as movement direction are assigned values, is fairly well understood. Motor and premotor cortex as well as portions of parietal cortex represent movement parameters through the activity of neuronal populations.
The parameter representation is of dynamic nature, updated in the course of movement and adapts to boundary condition of the motion plan or to environmental changes. Schwartz (2004) was able to decode motor cortical activity in motor cortex, utilizing to drive a virtual or robotic end-effector and proved that motor cortex is involved in the generation of time course of movement. At this level of abstraction we assume that movement of end-effector as well as human walking movement is represented appropriately by its direction and able to satisfy other constraints such as obstacle avoidance or movement coordination.
A neuronal dynamics of movement direction generates goal-directed movements and satisfies other constraints such as obstacle avoidance. Movement is generated by choosing low-dimensional, behaviorally relevant state variables and representing behavioral goals as attractors of dynamical systems over such behavioral variables. The robot’s trajectory emerges as a solution of the dynamics of these systems, in which the behavioral variables are stabilized at attractors corresponding to behavioral goals. Constraints are included in a similar manner as repellers.
Recently we applied this approach to generate reaching movements for manipulators under obstacle avoidance and orientation constraints. We aim to develop an approach to robotic action based on dynamical systems that is quantitatively modeled on human behavior. By varying the intrinsic parameters obtained for different individuals we will be able to implement different personal styles of movement.In this contribution we implement the neuronal dynamics of movement on a humanoid robotic system which generates goal-directed walking movements while avoiding obstacles.