Human-Guided Learning for Personalizing Robot Behaviors

The presence of robots performing tasks in real-world environments is rapidly growing. These robots will interact with various humans with different personal preferences, highlighting the need for robots that adapt their behavior accordingly. In this thesis, we develop tools and interfaces to convey task-critical information and personalize robot behavior. First, we explore settings where humans provide demonstrations for multiple tasks. For this setting, we introduce PECAN (Personalizing Robot Behavior through a Learned Canonical Space), a learning and interface-based approach that enables users to directly select their desired style. PECAN learn a continuous canonical space from demonstrations, where each point in the space corresponds to a style consistent across each task. Our simulation experiments and user studies indicate that humans prefer using PECAN to personalize robot behavior compared to existing methods. We then examine scenarios where robots complete a task in dynamic environments. A fundamental limitation when learning from demonstrations is causal confusion due to observations containing both task-relevant and extraneous information. Because the robot does not know what aspects of its observations are important a priori, it may fail to learn the intended task. We propose RECON (Reducing Causal Confusion with Human-Placed Markers), a framework that leverages beacons (UWB trackers) attached to task-relevant objects by the human before providing demonstrations. RECON learns a compact observation embedding correlated to the beacon information, and autonomously filters out extraneous information. Our experiments indicate that RECON significantly reduces the number of demonstrations required for teaching a task to the robot.

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

2025

Author dc:creator

• Ramirez Sanchez, Robert Javier

Chair dc:contributor.committeechair

• Losey, Dylan Patrick

Committee members dc:contributor.committeemember

• Southward, Steve C.
• Akbari Hamed, Kaveh

• Human-Robot Interaction
• Imitation Learning
• Representation Learning

Statement dc:rights

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:44051

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135096