Toward Orientation Learning and Adaptation in Cartesian Space
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
Series
IEEE Transactions on Robotics
Abstract
As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. Despite recent advances in learning orientations from demonstrations, several crucial issues have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this article, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations, which allows for learning orientation trajectories associated with high-dimensional inputs. In addition, we extend our approach to the learning of quaternions with angular acceleration or jerk constraints, which allows for generating smoother orientation profiles for robots. Several examples including experiments with real 7-DoF robot arms are provided to verify the effectiveness of our method.Description
Keywords
Quaternions, Robots, Trajectory, Task analysis, Probabilistic logic, Angular velocity, Collaboration
Other note
Citation
Huang, Y, Abu-Dakka, F J, Silvério, J & Caldwell, D G 2020, ' Toward Orientation Learning and Adaptation in Cartesian Space ', IEEE Transactions on Robotics, vol. 37, no. 1, 9166547, pp. 82-98 . https://doi.org/10.1109/TRO.2020.3010633
