FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation

ArXiv Pub Date : 2024-03-10 DOI:10.1145/3640543.3645145
Hanfang Lyu, Yuanchen Bai, Xin Liang, Ujaan Das, Chuhan Shi, Leiliang Gong, Yingchi Li, Mingfei Sun, Ming Ge, Xiaojuan Ma
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Abstract

Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users' familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers' consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly
FARPLS:特征增强型机器人轨迹偏好标注系统辅助人类标注者的偏好激发
基于偏好的学习旨在使机器人的任务目标与人类的价值观保持一致。推断人类偏好的最常用方法之一是对机器人任务轨迹进行成对比较。传统的基于比较的偏好标记系统很少支持标记者消化和识别视频中记录的复杂轨迹之间的关键差异。我们的形成性研究(N = 12)表明,在偏好激发过程中,由于观察不全面,人类可能会忽略非刺激性任务特征,并建立有偏差的偏好标准。此外,当需要比较多对标签时,他们可能会产生心理疲劳,导致标签质量下降。为了缓解这些问题,我们提出了特征增强机器人轨迹偏好标签系统 FARPLS。FARPLS 可突出显示对人类至关重要的各种任务特征中的潜在异常值,并提取相应的视频关键帧,以便于查看和比较。它还会根据用户的熟悉程度、轨迹对的难度和分歧程度动态调整标签顺序。与此同时,系统还能监控标注者的一致性,并提供标注进度反馈,以保持标注者的参与度。一项主体间研究(N = 42,每人 105 对机器人拾放轨迹)表明,与传统界面相比,FARPLS 可以帮助用户更容易地建立偏好标准,并注意到所显示轨迹中更多的相关细节。FARPLS 还能提高标注的一致性和参与度,在不显著增加认知负荷的情况下减轻偏好激发方面的挑战。
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