Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly. This demands learning models that capture relevant motion patterns, while offering enough flexibility to adapt the encoded skills to new req...
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Zusammenfassung: | For robots to work alongside humans and perform in unstructured environments,
they must learn new motion skills and adapt them to unseen situations on the
fly. This demands learning models that capture relevant motion patterns, while
offering enough flexibility to adapt the encoded skills to new requirements,
such as dynamic obstacle avoidance. We introduce a Riemannian manifold
perspective on this problem, and propose to learn a Riemannian manifold from
human demonstrations on which geodesics are natural motion skills. We realize
this with a variational autoencoder (VAE) over the space of position and
orientations of the robot end-effector. Geodesic motion skills let a robot plan
movements from and to arbitrary points on the data manifold. They also provide
a straightforward method to avoid obstacles by redefining the ambient metric in
an online fashion. Moreover, geodesics naturally exploit the manifold resulting
from multiple--mode tasks to design motions that were not explicitly
demonstrated previously. We test our learning framework using a 7-DoF robotic
manipulator, where the robot satisfactorily learns and reproduces realistic
skills featuring elaborated motion patterns, avoids previously unseen
obstacles, and generates novel movements in multiple-mode settings. |
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DOI: | 10.48550/arxiv.2106.04315 |