Adaptive Communication Interfaces for Human-Robot Collaboration

Robots can use a collection of auditory, visual, or haptic interfaces to convey information to human collaborators. The way these interfaces select signals typically depends on the task that the human is trying to complete: for instance, a haptic wristband may vibrate when the human is moving quickly and stop when the user is stationary. But people interpret the same signals in different ways, so what one user finds intuitive another user may not understand. In the absence of task knowledge, conveying signals is even more difficult: without knowing what the human wants to do, how should the robot select signals that helps them accomplish their task? When paired with the seemingly infinite ways that humans can interpret signals, designing an optimal interface for all users seems impossible. This thesis presents an information-theoretic approach to communication in task-agnostic settings: a unified algorithmic formalism for learning co-adaptive interfaces from scratch without task knowledge. The resulting approach is user-specific and not tied to any interface modality. This method is further improved by introducing symmetrical properties using priors on communication. Although we cannot anticipate how a human will interpret signals, we can anticipate interface properties that humans may like. By integrating these functional priors in the aforementioned learning scheme, we achieve performance far better than baselines that have access to task knowledge. The results presented here indicate that users subjectively prefer interfaces generated from the presented learning scheme while enabling better performance and more efficient interactions.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Christie, Benjamin Alexander

Chair dc:contributor.committeechair

• Losey, Dylan Patrick

Committee members dc:contributor.committeemember

• Akbari Hamed, Kaveh
• Acar, Pinar

• Interfaces
• Information Theory
• Co-Adaptation
• Human-Robot Interaction

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40044

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/118917