Long-Horizon Manipulation by a Single-arm Robot via Sub-goal Network based Hierarchical Reinforcement Learning
Jin Gyun Jeong, Ji-Heon Oh, Hwanseok Jung, J.H. Lee, Ismael Espinoza Jaramillo, Channabasava Chola, Won Hee Lee, Tae‐Seong Kim · 2023
In this work, we present an approach of long-horizon intelligence that utilizes Sub-goal network based hierarchical reinforcement learning (HRL) for long-horizon tasks by a single-arm robot. Long-horizon (LH) tasks are complicated due to their longer complex sequences and the large number of environmental variables. We attempt to solve the LH learning problem by the Sub-goal network based HRL. The proposed approach is tested in both simulation and hardware environments by a LH task of opening a drawer, grasping and relocating an object, and closing a drawer. Our Sub-goal network based HRL achieves a success rate of 90.3% in completing the LH tasks. Whereas the conventional deep reinforcement learning solution could not complete the LH task.