  An unstable grasp pose can lead to slip, thus an unstable grasp pose can be
predicted by slip detection. A regrasp is required afterwards to correct the
grasp pose in order to finish the task. In this work, we propose a novel
regrasp planner with multi-sensor modules to plan grasp adjustments with the
feedback from a slip detector. Then a regrasp planner is trained to estimate
the location of center of mass, which helps robots find an optimal grasp pose.
The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps
collected by one pair of tactile sensors, an RGB-D camera and one Franka Emika
robot arm equipped with joint force/torque sensors. We show that our algorithm
can successfully detect and classify the slip for 5 unknown test objects with
an accuracy of 76.88% and a regrasp planner increases the grasp success rate by
31.0% compared to the state-of-the-art vision-based grasping algorithm.

Robotics