A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
This work addresses bin picking for robotics, an incremental improvement focusing on cluttered and unstructured environments.
The paper tackles the problem of robot grasping in cluttered bin picking scenarios by proposing a fully self-supervised reinforcement learning approach using a hybrid discrete-continuous adaptation of soft actor-critic with parametrized motion primitives, resulting in improved adaptability in challenging setups.
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects. As a result, robot grasping has been an active field of research for many years. With our publication we contribute to the endeavor of enabling robots to grasp, with a particular focus on bin picking applications. Bin picking is especially challenging due to the often cluttered and unstructured arrangement of objects and the often limited graspability of objects by simple top down grasps. To tackle these challenges, we propose a fully self-supervised reinforcement learning approach based on a hybrid discrete-continuous adaptation of soft actor-critic (SAC). We employ parametrized motion primitives for pushing and grasping movements in order to enable a flexibly adaptable behavior to the difficult setups we consider. Furthermore, we use data augmentation to increase sample efficiency. We demonnstrate our proposed method on challenging picking scenarios in which planar grasp learning or action discretization methods would face a lot of difficulties