Periodic DMP formulation for Quaternion Trajectories
- URL: http://arxiv.org/abs/2110.10510v1
- Date: Wed, 20 Oct 2021 11:43:01 GMT
- Title: Periodic DMP formulation for Quaternion Trajectories
- Authors: Fares J. Abu-Dakka, Matteo Saveriano, Luka Peternel
- Abstract summary: Dynamic movement primitives (DMPs) have been widely exploited to learn and reproduce complex discrete and periodic skills.
We propose a novel formulation that enables encoding of periodic orientation trajectories.
We performed experiments on a real robot to execute daily tasks that involve periodic orientation changes.
- Score: 14.182479811070484
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Imitation learning techniques have been used as a way to transfer skills to
robots. Among them, dynamic movement primitives (DMPs) have been widely
exploited as an effective and an efficient technique to learn and reproduce
complex discrete and periodic skills. While DMPs have been properly formulated
for learning point-to-point movements for both translation and orientation,
periodic ones are missing a formulation to learn the orientation. To address
this gap, we propose a novel DMP formulation that enables encoding of periodic
orientation trajectories. Within this formulation we develop two approaches:
Riemannian metric-based projection approach and unit quaternion based periodic
DMP. Both formulations exploit unit quaternions to represent the orientation.
However, the first exploits the properties of Riemannian manifolds to work in
the tangent space of the unit sphere. The second encodes directly the unit
quaternion trajectory while guaranteeing the unitary norm of the generated
quaternions. We validated the technical aspects of the proposed methods in
simulation. Then we performed experiments on a real robot to execute daily
tasks that involve periodic orientation changes (i.e., surface polishing/wiping
and liquid mixing by shaking).
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