Learning from Few Demonstrations with Frame-Weighted Motion Generation

Learning from Few Demonstrations with Frame-Weighted Motion Generation

Sun, Jianyong (TU Delft Mechanical, Maritime and Materials Engineering)

Kober, J. (mentor)
Zhu, J. (mentor)
Gienger, Michael (mentor)
Peternel, L. (graduation committee)
Stienen, A.H.A. (graduation committee)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

2022-12-22

Learning from Demonstration (LfD) aims to learn versatile skills from human demonstrations. The field has been gaining popularity since it facilitates transferring knowledge to robots without requiring much expert knowledge. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized (TP) learning encodes relevant contextual information in reference frames, enabling better skill generalization to new situations. However, most TP learning algorithms require multiple demonstrations in various environment conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel concept to learn motion policy from few demonstrations through explicitly solving reference frame weights along the task trajectory. Experimental results in both simulation and real robotic environments validate our approach.

Learning from Demonstration
Few Demonstrations
Frame Weights
Data Augmentation

http://resolver.tudelft.nl/uuid:88639dbd-c0c3-44a9-a4fd-f92548e79bd0

Student theses

master thesis

© 2022 Jianyong Sun