Robust manipulation capture and retargeting has been a longstanding goal in both the
fields of animation and robotics. In this thesis I describe a new approach to capture
both the geometry and motion of interactions with objects, dealing with the problems
of occlusion by the use of magnetic systems, and performing the reconstruction of the
geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’
allows the scene to be described in terms of the spatial relationships between the character
and the object using novel topological representations such as the Electric Parameters,
which parametrise the outer space of an object using properties of the surface of
the object. I describe the properties of these representations for motion generalisation
and discuss how they can be applied to the problems of human-like motion generation
and programming by demonstration. These generalised interactions are shown
to be valid by demonstration of retargeting grasping and manipulation to robots with
dissimilar kinematics and morphology using only local, gradient-based planning
