Trajectory-Based Skill Learning Using Generalized Cylinders

By Seyed Reza Ahmadzadeh, Sonia Chernova. 2018

21 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance (2019, background): “For the reaching task, TLGC is hypothesized to have accrued low ratings due to robot executions which often stopped a short distance from the target.”