  In this paper, we explore the dynamic grasping of moving objects through
active pose tracking and reinforcement learning for hand-eye coordination
systems. Most existing vision-based robotic grasping methods implicitly assume
target objects are stationary or moving predictably. Performing grasping of
unpredictably moving objects presents a unique set of challenges. For example,
a pre-computed robust grasp can become unreachable or unstable as the target
object moves, and motion planning must also be adaptive. In this work, we
present a new approach, Eye-on-hAnd Reinforcement Learner (EARL), for enabling
coupled Eye-on-Hand (EoH) robotic manipulation systems to perform real-time
active pose tracking and dynamic grasping of novel objects without explicit
motion prediction. EARL readily addresses many thorny issues in automated
hand-eye coordination, including fast-tracking of 6D object pose from vision,
learning control policy for a robotic arm to track a moving object while
keeping the object in the camera's field of view, and performing dynamic
grasping. We demonstrate the effectiveness of our approach in extensive
experiments validated on multiple commercial robotic arms in both simulations
and complex real-world tasks.
