Hierarchical Reinforcement Learning using Gaussian Random Trajectory Generation in Autonomous Furniture Assembly - Yun, Won Joon; Mohaisen, David; Jung, Soyi; Kim, Jong Kook; Kim, Joongheon (International Conference on Information and Knowledge Management, Proceedings, 2022)
In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. The furniture assembly problem is formulated as a comprehensive human-like long-horizon manipulation task that requires a long-term planning and a sophisticated control. Our proposed model, GRT-HL, draws inspirations from the semi-supervised adversarial autoencoders, and learns latent representations of the position trajectories of the end-effector. The high-level policy generates an optimal trajectory for furniture assembly, considering the structural limitations of the robotic agents. Given the trajectory drawn from the high-level policy, the low-level policy makes a plan and controls the end-effector. We first evaluate the performance of GRT-HL compared to the state-of-the-art reinforcement learning methods in furniture assembly tasks. We demonstrate that GRT-HL successfully solves the long-horizon problem with extremely sparse rewards by generating the trajectory for planning.
Keywords: Assembly controls; Assembly problems; Gaussians; Hierarchical reinforcement learning; High level policies; Human like; Manipulation task; Reinforcement learning method; Reinforcement learnings; Trajectory generation; assembly control; hierarchical reinforcement learning; reinforcement learning; robotics