LAUSR

Robotic pick-and-place represents a nascent but swiftly evolving field in automation research. Most existing research relies on three-dimensional (3D) observations obtained directly from the 3D sensor or recovered by the two-dimensional (2D) camera from multiple perspectives. In this paper, we introduce an end-to-end pick-and-place neural network that solely leverages simple yet readily accessible data, namely, monocular camera and forward kinematics, for fusion applications. Additionally, our approach relies on the deep reinforcement learning (DRL) algorithm to facilitate robots in comprehending and completing tasks. The entire process is data-driven, devoid of any artificially designed task sessions, which imbues our approach with enhanced flexibility and versatility. The proposed method exhibits excellent performance in our experiment.