Title: Combining learned and analytical models for predicting action effects,
Abstract: One of the most basic skills a robot should possess is predicting the effect
of physical interactions with objects in the environment. This enables optimal
action selection to reach a certain goal state. Traditionally, dynamics are
approximated by physics-based analytical models. These models rely on specific
state representations that may be hard to obtain from raw sensory data,
especially if no knowledge of the object shape is assumed. More recently, we
have seen learning approaches that can predict the effect of complex physical
interactions directly from sensory input. It is however an open question how
far these models generalize beyond their training data. In this work, we
investigate the advantages and limitations of neural network based learning
approaches for predicting the effects of actions based on sensory input and
show how analytical and learned models can be combined to leverage the best of
both worlds. As physical interaction task, we use planar pushing, for which
there exists a well-known analytical model and a large real-world dataset. We
propose to use a convolutional neural network to convert raw depth images or
organized point clouds into a suitable representation for the analytical model
and compare this approach to using neural networks for both, perception and
prediction. A systematic evaluation of the proposed approach on a very large
real-world dataset shows two main advantages of the hybrid architecture.
Compared to a pure neural network, it significantly (i) reduces required
training data and (ii) improves generalization to novel physical interaction.