Generalized Cylinders for Learning, Reproduction, Generalization, and Refinement of Robot Skills

By Seyed Reza Ahmadzadeh, Muhammad Asif Rana, Sonia Chernova. 2017

15 citations recorded by the source corpus.

This paper presents a novel geometric approach for learning and reproducing trajectory-based skills from human demonstrations. Our approach models a skill as a Generalized Cylinder, a geometric representation composed of an arbitrary space curve called spine and a smoothly varying cross-section. While this model has been utilized to solve other robotics problems, this is the first application of Generalized Cylinders to manipulation. The strengths of our approach are the model’s ability to identify and extract the implicit characteristics of the demonstrated skill, support for reproduction of multiple trajectories that maintain those characteristics, generalization to new situations through nonrigid registration, and interactive human refinement of the resulting model through kinesthetic teaching. We validate our approach through several real-world experiments with a Jaco 6-DOF robotic arm.

Subfields: Artificial Intelligence, 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): “Based on the choice of motion generation strategy, the majority of skill learning approaches can be further broadly categorized into four groups: time-invariant (stable) dynamical systems [2], [3], [13], time-dependent dynamical systems [7], [9], [10], time-dependent statistical methods [8], [14], [”
Learning position and orientation dynamics from demonstrations via contraction analysis (2018, methodology): “Other categories of LfD methods include sampling-based methods (e.g., Bowen and Alterovitz 2014), optimization-based approaches (e.g., Dragan et al. 2015; Zucker et al. 2013), geometric methods (e.g., Ahmadzadeh et al. 2017), and key-frame-based methods (e.g., Akgun et al. 2012).”
Trajectory-Based Skill Learning Using Generalized Cylinders (2018, background): “We then considered a more ﬂexible and generalized form for the representation with the use of generalized cylinders (Ahmadzadeh et al., 2017).”