  Object shape provides important information for robotic manipulation; for
instance, selecting an effective grasp depends on both the global and local
shape of the object of interest, while reaching into clutter requires accurate
surface geometry to avoid unintended contact with the environment. Model-based
3D object manipulation is a widely studied problem; however, obtaining the
accurate 3D object models for multiple objects often requires tedious work. In
this letter, we exploit Gaussian process implicit surfaces (GPIS) extracted
from RGB-D sensor data to grasp an unknown object. We propose a
reconstruction-aware trajectory optimization that makes use of the extracted
GPIS model plan a motion to improve the ability to estimate the object's 3D
geometry, while performing a pick-and-place action. We present a probabilistic
approach for a robot to autonomously learn and track the object, while achieve
the manipulation task.
  We use a sampling-based trajectory generation method to explore the unseen
parts of the object using the estimated conditional entropy of the GPIS model.
We validate our method with physical robot experiments across eleven different
objects of varying shape from the YCB object dataset. Our experiments show that
our reconstruction-aware trajectory optimization provides higher-quality 3D
object reconstruction when compared with directly solving the manipulation task
or using a heuristic to view unseen portions of the object.
