S. Reza Ahmadzadeh, Sonia Chernova
Reference:
S. Reza Ahmadzadeh and Sonia Chernova, "Trajectory-based skill learning using generalized cylinders", Frontiers in Robotics and AI, 5:1--18, 2018.
Bibtex Entry:
@ARTICLE{ahmadzadeh2018trajectory, TITLE={Trajectory-Based Skill Learning Using Generalized Cylinders}, AUTHOR={Ahmadzadeh, S. Reza and Chernova, Sonia}, JOURNAL={Frontiers in Robotics and AI}, VOLUME={5}, PAGES={1--18}, YEAR={2018}, URL={https://www.frontiersin.org/article/10.3389/frobt.2018.00132}, DOI={10.3389/frobt.2018.00132}, ISSN={2296-9144}, }
Abstract:
In this article, we introduce Trajectory Learning using Generalized Cylinders (TLGC), a novel trajectory-based skill learning approach from human demonstrations. To model a demonstrated skill, TLGC uses a Generalized Cylinder—a geometric representation composed of an arbitrary space curve called the spine and a surface with smoothly varying cross-sections. Our approach is the first application of Generalized Cylinders to manipulation, and its geometric representation offers several key features: it identifies and extracts the implicit characteristics and boundaries of the skill by encoding the demonstration space, it supports for generation of multiple skill reproductions maintaining those characteristics, the constructed model can generalize the skill to unforeseen situations through trajectory editing techniques, our approach also allows for obstacle avoidance and interactive human refinement of the resulting model through kinesthetic correction. We validate our approach through a set of real-world experiments with both a Jaco 6-DOF and a Sawyer 7-DOF robotic arm.