Learning to plan for robots using generalized representations

Robots operating in the real world should be able to make decisions and plan ahead their actions. We argue that learning using generalized representations of the robot's experience can assist such a ability.

We present results from our research on methods for enabling mobile robots to plan their actions using generalized representations of their experience. Such generalized representations are acquired through a learning phase during which the robot explores its environment and builds subsymbolic (connectionist) representations of the result that its actions have to its sensory perception. Then these representations are employed by the robot for autonomously determining task‐achieving sequences of actions (plans),for attaining assigned tasks.

Such subsymbolic mechanisms can employ generalization techniques in order to pursue plans through unexplored regions of the robot's environment.

Subsymbolic motion planning can autonomously determine task‐achieving sequences of actions in real environments, without using presupplied symbolic knowledge, but instead generating novel plans using previously acquired subsymbolic representations.