We present an approach that enables robots to
learn motion primitives that are robust towards state estimation
uncertainties. During reaching and preshaping, the robot learns
to use fine manipulation strategies to maneuver the object into
a pose at which closing the hand to perform the grasp is more
likely to succeed. In contrast, common assumptions in grasp
planning and motion planning for reaching are that these tasks
can be performed independently, and that the robot has perfect
knowledge of the pose of the objects in the environment.
We implement our approach using Dynamic Movement
Primitives and the probabilistic model-free reinforcement learning
algorithm Policy Improvement with Path Integrals (PI2 ).
The cost function that PI2 optimizes is a simple boolean that
penalizes failed grasps. The key to acquiring robust motion
primitives is to sample the actual pose of the object from a
distribution that represents the state estimation uncertainty.
During learning, the robot will thus optimize the chance of
grasping an object from this distribution, rather than at one
In our empirical evaluation, we demonstrate how the motion
primitives become more robust when grasping simple cylindrical
objects, as well as more complex, non-convex objects. We
also investigate how well the learned motion primitives generalize
towards new object positions and other state estimation