Guided Learning from Demonstration for Robust Transferability
Fouad Sukkar, Victor Hernandez Moreno, Teresa Vidal-Calleja, Jochen, Deuse

TL;DR
This paper presents a guided learning from demonstration approach using a GUI and motion planning to ensure demonstrations are within reproducible motion spaces, improving success rates in robotic tasks.
Contribution
Introducing a novel guided LfD paradigm that prevents non-reproducible demonstrations using a motion planning-based GUI interface.
Findings
Guided demonstrations significantly improve task success rates.
Method validated on UR5 and Sawyer robotic systems.
User study shows increased success with guidance.
Abstract
Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness. The latter is particularly important to consider when the target system reproducing the motion is structurally different to the demonstration system, as some demonstrated motions may not be reproducible. In light of this, this paper introduces a new guided learning from demonstration paradigm where an interactive graphical user interface (GUI) guides the user during demonstration, preventing them from demonstrating non-reproducible motions. The key aspect of our approach is determining the space of reproducible motions based on a motion planning framework which finds regions in the task space…
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Taxonomy
TopicsRobot Manipulation and Learning · Robotic Mechanisms and Dynamics · Soft Robotics and Applications
