A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives

TL;DR

This paper presents a self-supervised reinforcement learning method using hybrid motion primitives and data augmentation to improve robotic grasping in cluttered bin picking scenarios, addressing challenges of unstructured object arrangements.

Contribution

It introduces a hybrid discrete-continuous SAC approach with parametrized motion primitives and data augmentation for enhanced bin picking capabilities.

Findings

Effective in complex cluttered environments

Outperforms traditional grasping methods

Demonstrates improved sample efficiency

Abstract

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