A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations
Alexander Fabisch, Manuela Uliano, Dennis Marschner, Melvin Laux, Johannes Brust, Marco Controzzi
In Proceedings of the IEEE-RAS International Conference on Humanoid Robots, (Humanoids-2022), 28.11.-30.11.2022, Ginowan, IEEE, 2023. IEEE-RAS.
Manipulating objects with robotic hands is a
complicated task. Not only the fingers of the hand, but also
the pose of the robot’s end effector need to be coordinated.
Using human demonstrations of movements is an intuitive and
data-efficient way of guiding the robot’s behavior. We propose
a modular framework with an automatic embodiment mapping
to transfer recorded human hand motions to robotic systems. In
this work, we use motion capture to record human motion. We
evaluate our approach on eight challenging tasks, in which a
robotic hand needs to grasp and manipulate either deformable
or small and fragile objects. We test a subset of trajectories in
simulation and on a real robot and the overall success rates of
about 70% are aligned.