Exploiting Task Tolerances in Mimicry-based Telemanipulation

TL;DR

This work investigates how exploiting task tolerances through functional mimicry can improve telemanipulation. By enabling a robot to autonomously adjust within task tolerances using RangedIK, the authors aim to produce more accurate, smooth, and feasible end-effector motions while preserving user control. In a within-subject user study, functional mimicry improved performance in Writing and Erasing tasks and increased perceived controllability, predictability, fluency, and trust, with notable effect sizes, though benefits were task-dependent. The results suggest that embracing tolerance-driven flexibility can enhance motion quality and user experience in mimicry-based teleoperation, with implications for a wide range of semi-constrained manipulation tasks.

Abstract

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user's control less direct. In this work, we implemented a telemanipulation system that allows a robot to autonomously adjust its configuration within task tolerances. We conducted a user study comparing a telemanipulation paradigm that exploits task tolerances (functional mimicry) to a paradigm that requires the robot to exactly mimic its human operator (exact mimicry), and assess how the choice in paradigm shapes user experience and task performance. Our results show that autonomous adjustments within task tolerances can lead to performance improvements without sacrificing perceived control of the robot. Additionally, we find that users perceive the robot to be more under control, predictable, fluent, and trustworthy in functional mimicry than in exact mimicry.

Figures (3)

• Figure 1: Our human-subject experiment evaluated exact and functional mimicry on five manipulation tasks that have tolerances. Table \ref{['tab:task_ranges']} lists the tolerances in the visualized coordinate frame, where red, green, and blue arrows represent $x$, $y$, and $z$ axes, respectively.
• Figure 2: Box and whisker plots of data from the user perception and performance measures for our experiment. The top and bottom of each box represent the first and third quartiles, and the line inside each box is the statistical median of the data. The length of the box is defined as the interquartile range (IQR). The whiskers are within a maximum of 1.5 IQR. TLX Overall means averaged NASA Task Load Index scores.
• Figure 3: Our subgroup analyses explore the effects of functional mimicry on different types of users. The performance improvements are the differences between the combined objective metrics in each paradigm, which are averaged over the five tasks. The perception improvements are the differences between subjective measures in each condition, which are averaged over the four subjective scales. For both the performance and perception improvements, a more positive value indicates a larger improvement in the functional mimicry condition compared to the exact mimicry condition.