Slabstone Installation Skill Acquisition for DualArm Robot based on Reinforcement Learning Slabstone installation is so widely used in the construction industry that it is very significant to use the robot to finish this process, making the process more automatic, effective and safer. Because of the uncertain factors in environments and filling material, it is difficult to finish the final step of slabstone installation that pressing the slabstone into good contact with filling material. There is currently no mature theory and method to achieve control of the process. In this paper, we use a dualarm robot to accomplish the final step. First, we use the springdamping elements to model and simulate the filling material. Then, based on deep reinforcement learning, a skill acquisition method is proposed to solve the problems of uncertain factors in the final step of slabstone installation process. Finally, we use a simulationplatform to train and evaluate our algorithm. The results have shown that our method is capable and effective in accomplishing the installation process.

On Training Flexible Robots using Deep Reinforcement Learning The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many realworld settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest in developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL), we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.