A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures

TL;DR

This paper introduces a unified probabilistic framework for virtual fixtures that adaptively switch between manual, semi-automated, and fully autonomous modes, enhancing task efficiency and precision in robotic assistance.

Contribution

It presents a novel probabilistic dynamical system-based virtual fixture framework that integrates multiple operation modes with seamless human-robot interaction.

Findings

Successfully validated on different robots

Demonstrated seamless switching between modes

Showcased ease of programming fixtures

Abstract

Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While keeping the human in the loop remains essential, for instance, to ensure high precision, partial automation of certain task phases is critical for productivity. We present a unified framework for probabilistic VFs that seamlessly switches between manual fixtures, semi-automated fixtures (with the human handling precise tasks), and full autonomy. We introduce a novel probabilistic Dynamical System-based VF for coarse guidance, enabling the robot to autonomously complete certain task phases while keeping the human operator in the loop. For tasks requiring precise guidance, we extend probabilistic position-based trajectory fixtures with automation allowing for seamless human interaction as well as geometry-awareness and optimal impedance gains. For manual tasks requiring very precise guidance, we also extend visual servoing fixtures with the same geometry-awareness and impedance behavior. We validate our approach experimentally on different robots, showcasing multiple operation modes and the ease of programming fixtures.