  Robots need to estimate the material and dynamic properties of objects from
observations in order to simulate them accurately. We present a Bayesian
optimization approach to identifying the material property parameters of
objects based on a set of observations. Our focus is on estimating these
properties based on observations of scenes with different sets of interacting
objects. We propose an approach that exploits the structure of the reward
function by modeling the reward for each observation separately and using only
the parameters of the objects in that scene as inputs. The resulting
lower-dimensional models generalize better over the parameter space, which in
turn results in a faster optimization. To speed up the optimization process
further, and reduce the number of simulation runs needed to find good parameter
values, we also propose partial evaluations of the reward function, wherein the
selected parameters are only evaluated on a subset of real world evaluations.
The approach was successfully evaluated on a set of scenes with a wide range of
object interactions, and we showed that our method can effectively perform
incremental learning without resetting the rewards of the gathered
observations.
