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The neuronal basis of movement preparation, during which movement parameters such as movement direction are assigned values, is fairly well understood (Georgopoulos, 2000). Motor and premotor cortex as well as portions of the parietal cortex represent movement parameters through the activity of neuronal populations (Bastian et al., 2003; Cisek & Kalaska, 2005).
The parameter representation is of dynamic nature, updated in the course of movement. It adapts to boundary conditions of the motion plan or to environmental changes. Schwartz (2004) was able to decode motor cortical activity in the motor cortex and utilized this knowledge to drive a virtual or robotic end-effector. Thus he proved that the motor cortex is involved in the generation of movement planning. At this level of abstraction we assume that the movement of an end-effector, as well as human walking movement, is represented appropriately by its direction and satisfies other constraints, such as obstacle avoidance or movement coordination.
A neuronal dynamic of movement generates goal-directed movements and satisfies other constraints, such as obstacle avoidance. Movement is generated by choosing low-dimensional, behaviorally relevant state variables. Behavioral goals are represented as attractors of dynamical systems over such behavioral variables (Schöner et al., 1995). The robots trajectory emerges as a solution of these dynamical 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 con- straints (Iossifidis & Schöner, 2009; Reimann et al., 2010a,b).
We aim to develop an approach to robotic action based on dynamical systems 1
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.
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.