Vision-Based Reinforcement Learning for Robotic Grasping of Moving Objects on a Conveyor
Year: 2025
Type: article
Abstract: This study introduces an autonomous framework for grasping moving objects on a conveyor belt, enabling unsupervised detection, grasping, and categorization. The work focuses on two common object shapes—cylindrical cans and rectangular cartons—transported at a constant speed of 3–7 cm/s on the conveyor, emulating typical scenarios. The proposed framework combines a vision-based neural network for object detection, a target localization algorithm, and a deep reinforcement learning model for robotic control. Specifically, a YOLO-based neural network was employed to detect the 2D position of target objects. These positions are then converted to 3D coordinates, followed by pose estimation and error correction. A Proximal Policy Optimization (PPO) algorithm was then used to provide continuous control decisions for the robotic arm. A tailored reinforcement learning environment was developed using the Gymnasium interface. Training and validation were conducted on a 7-degree-of-freedom (7-DOF) robotic arm model in the PyBullet physics simulation engine. By leveraging transfer learning and curriculum learning strategies, the robotic agent effectively learned to grasp multiple categories of moving objects. Simulation experiments and randomized trials show that the proposed method enables the 7-DOF robotic arm to consistently grasp conveyor belt objects, achieving an approximately 80% success rate at conveyor speeds of 0.03–0.07 m/s. These results demonstrate the potential of the framework for deployment in automated handling applications.
Source: Machines
Authors: Yin Cao, Xuemei Xu, Yazheng Zhang
Institution: Lomonosov Moscow State University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: China Scholarship Council
Award: G5116964011
OpenAlex ID: W4415377887