Decentralized Federated Multi-Task Learning and System Design

Decentralized Federated Multi-Task Learning and System Design

Within the field of Human-Robot Interaction (HRI), a growing subfield is forming that focuses specifically on interactions between one or more robots and multiple people, known as Multi-Party Human-Robot Interaction (MP-HRI). MP-HRI encompasses the challenges of single-user HRI (interaction dynamics, human perception, etc.) and extends them to the challenges of multi-party interactions (within-group turn taking, dyadic dynamics, and group dynamics).

To address these, MP-HRI requires new methods and approaches. Effective MP-HRI enables robotic systems to function in many contexts, including service, support, and mediation. In realistic human contexts, service and support robots need to work with varying numbers of individuals, particularly when working within team structures. In mediation, robotic systems must by definition, be able to work with multiple parties. These contexts often overlap, and algorithms that work in one context can benifit work in another.

This project will advance the basic research in trust and influence in MP-HRI contexts. This will involve exploring how robots and people establish, maintain, and repair trust in MP-HRI. Specifically, this research will examine robot group mediation for group conseling, with extensions to team performance in robot service and support teams.

See Interaction Lab website for details and related publications