A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control
This work addresses the challenge of complex assembly tasks in robotics, such as peg-in-hole with submillimeter tolerances, by providing a learning-based solution that achieves real-world performance, though it builds incrementally on prior adaptive control methods.
The paper tackles the problem of learning force-sensitive robot manipulation skills by introducing a framework that combines adaptive impedance control, meta parameter learning, and skill formalism, resulting in a system that reduces task complexity and learns variations of peg-in-hole tasks in under 20 minutes, performing faster than humans.
In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also able to make use of abstract expert knowledge by incorporating process descriptions and quality evaluation metrics. We evaluate various state-of-the-art schemes for the meta parameter learning and experimentally compare selected ones. Our results clearly indicate that the combination of our adaptive impedance controller with a carefully defined skill formalism significantly reduces the complexity of manipulation tasks even for learning peg-in-hole with submillimeter industrial tolerances. Overall, the considered system is able to learn variations of this skill in under 20 minutes. In fact, experimentally the system was able to perform the learned tasks faster than humans, leading to the first learning-based solution of complex assembly at such real-world performance.