Human-Robot Interaction (HRI)

HRI is defined as the study of how humans and robots communicate and collaborate to perform tasks safely and effectively. Our research leverages AI to make assistive robots usable in everyday tasks by people with profound motor impairments, such as ALS, spinal cord injuries or Parkinson’s disease, while keeping the physical and cognitive burden on the user low. We personalize robot behaviors from users’ natural language feedback, and let users direct robots with their eye gaze, predicting the intended task from egocentric gaze in a way that tolerates the abnormal gaze patterns that come with motor degeneration.

Gaze-Guided Intent Prediction for Individuals with Severe Motor Impairments
We bring gaze-guided task suggestion for assistive robots into longitudinal clinical deployments with people living with ALS, spinal cord injuries or Parkinson’s disease. By tracking how motor degeneration affects egocentric gaze and head coordination over time, we adapt our filtering and visual-language algorithms to tolerate pathological gaze noise such as jitter, saccadic intrusions and fixational drift.
September 2026 In arXiv
PDF
Gaze-Guided Intent Prediction for Individuals with Severe Motor Impairments
Low-Burden LLM-Based Preference Learning: Personalizing Assistive Robots from Natural Language Feedback for Users with Paralysis
We developed an LLM-based framework that personalizes assistive robots using natural language feedback, significantly reducing the physical and cognitive fatigue associated with traditional methods. Grounded in clinical reasoning, the system translates unstructured user input into safe, deterministic control policies for individuals with profound motor impairments.
August 2026 In RO-MAN 2026
PDF Cite Code DOI
Low-Burden LLM-Based Preference Learning: Personalizing Assistive Robots from Natural Language Feedback for Users with Paralysis