The ability to determine a sequence of actions in order to reach a particular goal is of utmost importance to mobile robots. One major problem with
planning approaches regards assumptions made by the designer while introducing externally specified world models, preconditions and postconditions. To bypass this problem, it would be desirable to develop mechanisms for action planning that are based on the
perceptual and behavioural space, rather than on externally supplied symbolic representations. We present a subsymbolic planning mechanism that uses a non-symbolic representation of sensor-action space, learned through the agent’s autonomous interaction with the environment.
In this paper, we present experiments with two autonomous mobile robots, which use autonomously learned subsymbolic representations of perceptual and behavioural space to determine goal-achieving sequences of actions. The experimental results we present illustrate that such an approach results in an embodied robot planner that produces plans which are grounded in the robot’s perception-action space.