Motion Mappings for Continuous Bilateral Teleoperation

TL;DR

This paper introduces a unified approach for continuous bilateral teleoperation mapping that improves smoothness and transparency by using object-based pose mappings, with neural networks offering faster, lower-force control.

Contribution

It proposes a novel unified formulation for motion mappings in teleoperation and compares iterative and neural network implementations for improved performance.

Findings

Neural network approach yields faster mapping evaluations.

Neural network reduces interaction forces during teleoperation.

Unified pose-based mapping improves continuity and transparency.

Abstract

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.