[Thesis Defense] Personalizing Robot Assistance under Uncertainty about the Human

Date: June 2
Time: 9:00-11:00 AM ET
Location: 32-155
Zoom: https://mit.zoom.us/j/9731989629

Title: Personalizing Robot Assistance under Uncertainty about the Human

Abstract
Robots have the potential to improve the quality of life by assisting with daily tasks, such as helping older adults and people with disabilities get dressed. But meaningful assistance requires personalization: each person has unique preferences, behaviors, and needs.

A central challenge is that robots often operate under uncertainty about the human they are helping. This uncertainty may involve the person's preferences, hidden physical states, or reactions to assistance. If not properly addressed, such uncertainty can lead to ineffective, undesired, or even unsafe outcomes.

This thesis asks: How should a robot behave when it is uncertain about the human? I present a unified framework for uncertainty-aware personalization in human-robot interaction, spanning three core components of robot intelligence: preference learning, state estimation, and motion planning.

1. Preference learning: I introduce the first method that uses response time, a subtle but informative cognitive signal, as implicit feedback. By combining human choices with response times, robots can infer not only what a person prefers but also how strongly they prefer it. This reduces uncertainty and accelerates preference learning.

2. State estimation: To support safe physical assistance when parts of the human body (e.g., the elbow) are occluded, I introduce a state estimator that models uncertainty in learned human dynamics and robot sensing. It constructs a geometric set (e.g., a 3D box) that reliably contains the true hidden human state, enabling safer and more precise robot behavior.

3. Motion planning: When a robot is uncertain about future human motion, it may behave overly conservatively to avoid causing harm, resulting in ineffective assistance. To address this, I propose a relaxed safety formulation that allows the robot to either avoid collisions or make low-impact contact. This approach enables the robot to act more effectively while still maintaining safety under uncertainty.

Together, these contributions lay a foundation for assistive robots that personalize their behavior while adapting to the uncertain and dynamic nature of human needs.

Thesis Supervisor: Julie A. Shah
Committee Members: Julie A. Shah, Dylan Hadfield-Menell, Na (Lina) Li, Aude Billard
Thesis Readers: Vaibhav Unhelkar, Tariq Iqbal
Contact: shenli@mit.edu