Teaching Robot End Effectors to Grasp Construction Tools Based on Deep Reinforcement Learning

Abstract

In onsite construction, however, there are numerous delicate tasks that require skilled workers to perform. A key challenge in automating these tasks is developing motor skills in robots trained through RL, as manipulating irregular and delicate objects like hammers, scaffolding, and drills remains difficult. To address this issue, this paper proposes an RL-based approach for performing delicate tasks using a robotic arm with grippers. We present a simulation-based policy learning framework utilizing the Critic-Actor algorithm in Pybullet to control the robotic arm. In experimental trials, the learned policy was used to grasp six different types of construction tools, and the results demonstrated the feasibility of training with randomly shaped objects to manipulate the construction tools with a reasonable success rate. This method provides a foundation for enhancing the manipulative skills of construction robots, potentially reducing labor costs in the industry.