Hierarchical Learning for Robotic Assembly Tasks Leveraging Learning from Demonstration

Abstract

Robotic assembly in manufacturing settings is a special type of long‐horizon task and motion planning problem. While devising a motion plan for the robot is itself challenging, identifying a task and learning it adds to problem's complexity. This article proposes a hierarchical learning ‐based approach that leverages the multilevel structure to seamlessly integrate task identification and sequencing with robot motion planning. Given the final assembly goal the higher‐level agent emphasizes comprehending tasks and learning task plans. It generates sequences of sub‐tasks, while the lower‐level agent concentrates on executing the current sub‐task. The higher‐level agent employs a goal driven reinforcement learning approach to learn the sequencing task, allowing it to adapt to unseen assemblies. Meanwhile, the lower level adopts a learning from demonstration approach for motion planning, which can learn primitive skills from one‐time demonstration and intelligently combine these primitive skills for complicated tasks. The critical contribution of this work lies in the development of a novel method capable of comprehending and executing long horizon goal‐driven assembly tasks without relying on expert demonstrations or explicit description of the whole assembly. The proposed approach is validated through simulation and physical setup. Project Website: https://sites.google.com/virginia.edu/isl‐hllfdra/home .