Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
This work addresses forceful manipulation skills for industrial robotics, such as assembly, but is incremental as it builds on prior task-parameterized optimization and impedance control methods.
The authors tackled the problem of learning forceful manipulation skills from multi-modal human demonstrations, extending Learning from Demonstration (LfD) beyond pose-only to include force and torque, and validated it on a 7-DoF robot arm for E-bike motor assembly tasks, achieving reliable reproduction of skills like insertion, sliding, and twisting.
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spatial and temporal features. In this work, we extend the LfD framework to address forceful manipulation skills, which are of great importance for industrial processes such as assembly. For such skills, multi-modal demonstrations including robot end-effector poses, force and torque readings, and operation scene are essential. Our objective is to reproduce such skills reliably according to the demonstrated pose and force profiles within different scenes. The proposed method combines our previous work on task-parameterized optimization and attractor-based impedance control. The learned skill model consists of (i) the attractor model that unifies the pose and force features, and (ii) the stiffness model that optimizes the stiffness for different stages of the skill. Furthermore, an online execution algorithm is proposed to adapt the skill execution to real-time observations of robot poses, measured forces, and changed scenes. We validate this method rigorously on a 7-DoF robot arm over several steps of an E-bike motor assembly process, which require different types of forceful interaction such as insertion, sliding and twisting.