Observing, learning, and imitating human
skills are intriguing topics in cognitive robotics. The main
problem in the imitation learning paradigm is the policy
development. Policy can be defined as a mapping from an agent's
current world state to actions. Thus, understanding and performing
an observed human skill for a cognitive agent depends heavily upon
the learned policy. So far, naive policies that use object and hand
models with trajectory information have commonly been developed to
encode and imitate various types of human manipulations. These
approaches, on the one hand, can not be general enough since models
are not learned by the agent itself but rather are provided by the
designer in advance. It is also not sufficient to imitate
complicated manipulations at the trajectory-level since even the
same observed manipulation can have high variations in trajectories
from demonstration to demonstration. Nevertheless, humans have the
capability of recognizing and imitating observed manipulations
without any problem. In humans, the chain of perception, learning,
and imitation of manipulations is developed in conjunction with the
interpretation of the manipulated objects. To compose a human-like
perception-action chain the cognitive agent needs a generic policy
that can extract manipulation primitives as well as the essential
(invariant) relations between objects and manipulation actions. In
this thesis, we introduce a novel concept, the so-called “Semantic
Event Chain
Keywords: Semantic Event Chains; Scene Graphs; Object-Action Relations; Manipulation Actions
Schlagwörter: Semantic Event Chains; Scene Graphs; Relationen von Objekten und Handlungen
