Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
An T. Le, Meng Guo, Niels van Duijkeren, Leonel Rozo, Robert Krug,, Andras G. Kupcsik, Mathias Buerger

TL;DR
This paper extends Learning from Demonstration to forceful manipulation skills by integrating multi-modal demonstrations, enabling robots to perform complex industrial tasks involving force and pose profiles with scene adaptation.
Contribution
It introduces a novel framework combining task-parameterized optimization and impedance control for forceful skills, including pose and force features, with an online adaptation algorithm.
Findings
Validated on a 7-DoF robot for E-bike assembly tasks
Successfully performed insertion, sliding, and twisting operations
Demonstrated reliable reproduction of forceful manipulation skills
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Soft Robotics and Applications · Prosthetics and Rehabilitation Robotics
