Understanding Robot Minds: Leveraging Machine Teaching for Transparent Human-Robot Collaboration Across Diverse Groups

TL;DR

This paper develops machine teaching algorithms for human-robot collaboration that effectively handle diverse team compositions, improving transparency and learning efficiency through tailored group belief strategies.

Contribution

It introduces group belief-based teaching methods for heterogeneous teams, addressing personalization challenges and optimizing robot policy learning in collaborative settings.

Findings

Team belief strategies reduce variation in learning duration.

Group belief approaches better accommodate diverse teams.

Individual belief strategies are effective for homogeneous, inexperienced groups.

Abstract

In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teaching individuals, our method teaches teams with various compositions of diverse learners using team belief representations to address personalization challenges within groups. We investigate various group teaching strategies, such as focusing on individual beliefs or the group's collective beliefs, and assess their impact on learning robot policies for different team compositions. Our findings reveal that team belief strategies yield less variation in learning duration and better accommodate diverse teams compared to individual belief strategies, suggesting their suitability in mixed-proficiency settings with limited resources. Conversely, individual belief strategies provide a more uniform knowledge level, particularly effective for homogeneously inexperienced groups. Our study indicates that the teaching strategy's efficacy is significantly influenced by team composition and learner proficiency, highlighting the importance of real-time assessment of learner proficiency and adapting teaching approaches based on learner proficiency for optimal teaching outcomes.