  Autonomous dual-arm manipulation is an essential skill to deploy robots in
unstructured scenarios. However, this is a challenging undertaking,
particularly in terms of perception and planning. Unstructured scenarios are
full of objects with different shapes and appearances that have to be grasped
in a very specific manner so they can be functionally used. In this paper we
present an integrated approach to perform dual-arm pick tasks autonomously. Our
method consists of semantic segmentation, object pose estimation, deformable
model registration, grasp planning and arm trajectory optimization. The entire
pipeline can be executed on-board and is suitable for on-line grasping
scenarios. For this, our approach makes use of accumulated knowledge expressed
as convolutional neural network models and low-dimensional latent shape spaces.
For manipulating objects, we propose a stochastic trajectory optimization that
includes a kinematic chain closure constraint. Evaluation in simulation and on
the real robot corroborates the feasibility and applicability of the proposed
methods on a task of picking up unknown watering cans and drills using both
arms.
