  Mapping operator motions to a robot is a key problem in teleoperation. Due to
differences between workspaces, such as object locations, it is particularly
challenging to derive smooth motion mappings that fulfill different goals (e.g.
picking objects with different poses on the two sides or passing through key
points). Indeed, most state-of-the-art methods rely on mode switches, leading
to a discontinuous, low-transparency experience. In this paper, we propose a
unified formulation for position, orientation and velocity mappings based on
the poses of objects of interest in the operator and robot workspaces. We apply
it in the context of bilateral teleoperation. Two possible implementations to
achieve the proposed mappings are studied: an iterative approach based on
locally-weighted translations and rotations, and a neural network approach.
Evaluations are conducted both in simulation and using two torque-controlled
Franka Emika Panda robots. Our results show that, despite longer training
times, the neural network approach provides faster mapping evaluations and
lower interaction forces for the operator, which are crucial for continuous,
real-time teleoperation.
