Abstract:
Given a con guration involving n objects in an environment, the
planning problem considered in this theses is to nd a plan that re-
arranges these objects so as to place a new object. The challenging
aspect here involves deciding when an object can be placed on top
of another object. Here only de ning standard planning operators
would not su ce. For instance, using these operators we can come
up with actions that may be performed at a state but it should not
be performed. So we introduce the notion of safe actions whose
outcomes are consistent with the laws of physics, commonsense, and
common practice. A safe action can be performed if a robot perform-
ing the action knows the knowledge of the situation. We developed
a knowledge engine using a supervised learning technique. However,
unlike the common task of learning functions, our approach is to
learn predicates|that evaluate to binary values. By learning such a
predicate a robot would be able to decide whether or not an object A
can be placed on top of another object B. Once the robot learns the
predicate, planning can be carried out and the plans contain only
safe actions. We also suggest a method to handle objects previously
not seen in the training set.
