Motion Mappings for Continuous Bilateral Teleoperation
This work aims to improve the continuity and transparency of teleoperation for operators by reducing the need for mode switches, which is an incremental improvement for the field of robotics.
This paper addresses the challenge of creating smooth motion mappings for teleoperation by proposing a unified formulation for position, orientation, and velocity mappings based on object poses in both operator and robot workspaces. The neural network implementation of their method resulted in faster mapping evaluations and lower interaction forces for the operator compared to an iterative approach.
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.